Packages

package forecaster

The forecaster package contains classes, traits and objects for forecaster.

See also

DiffTest for testing of the 'diff' and 'undiff' methods.

Linear Supertypes
Error, AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. forecaster
  2. Error
  3. AnyRef
  4. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Type Members

  1. class AR extends ForecasterVec

    The AR class provides basic time series analysis capabilities for Auto- Regressive 'AR'.

    The AR class provides basic time series analysis capabilities for Auto- Regressive 'AR'. In an 'AR(p)' model, 'p' refers to the order of the Auto-Regressive components of the model. AR models are often used for forecasting. Given time series data stored in vector 'y', its next value 'y_t = y(t)' may be predicted based on prior values of 'y' and its noise:

    y_t = δ + Σ(φ_k y_t-k)

    where 'δ' is a constant, 'φ' is the autoregressive coefficient vector, and 'e' is the noise vector. ----------------------------------------------------------------------------------

  2. class ARIMA extends ARMA

    The ARIMA class provides basic time series analysis capabilities for Auto- Regressive 'AR' Integrated 'I' Moving-Average 'MA' models.

    The ARIMA class provides basic time series analysis capabilities for Auto- Regressive 'AR' Integrated 'I' Moving-Average 'MA' models. In an 'ARIMA(p, d, q)' model, 'p' and 'q' refer to the order of the Auto-Regressive and Moving-Average components of the model; 'd' refers to the order of differencing. Given time series data stored in vector 'y', its next value 'y_t = y(t)' may be predicted based on prior values of 'y' and its noise:

    y_t = δ + Σ(φ_i y_t-i) + Σ(θ_i e_t-i) + e_t

    where 'δ' is a constant, 'φ' is the auto-regressive coefficient vector, 'θ' is the moving-average coefficient vector, and 'e' is the noise vector. ------------------------------------------------------------------------------ If 'd > 0', then the time series must be differenced first before applying the above model. ------------------------------------------------------------------------------

  3. class ARMA extends ForecasterVec

    The ARMA class provides basic time series analysis capabilities for Auto- Regressive 'AR' and Moving-Average 'MA' models.

    The ARMA class provides basic time series analysis capabilities for Auto- Regressive 'AR' and Moving-Average 'MA' models. In an 'ARMA(p, q)' model, 'p' and 'q' refer to the order of the Auto-Regressive and Moving-Average components of the model. Given time series data stored in vector 'y', its next value 'y_t = y(t)' may be predicted based on prior values of 'y' and its noise:

    y_t = δ + Σ(φ_i y_t-i) + Σ(θ_i e_t-i) + e_t

    where 'δ' is a constant, 'φ' is the auto-regressive coefficient vector, 'θ' is the moving-average coefficient vector, and 'e' is the noise vector. ------------------------------------------------------------------------------

  4. class AR_1 extends ForecasterVec with NoFeatureSelectionF

    The AR_1 class provides basic time series analysis capabilities for Auto- Regressive 'AR'.

    The AR_1 class provides basic time series analysis capabilities for Auto- Regressive 'AR'. In an 'AR_1' model, 1 refers to the order of the Auto-Regressive components of the model. AR_1 models are often used for forecasting. Given time series data stored in vector 'y', its next value 'y_t = y(t)' may be predicted based on prior values of 'y' and its noise:

    y_t = c + Σ(φ_k y_t-k)

    where 'c' is a constant, 'φ' is the autoregressive coefficient vector, and 'e' is the noise vector. ----------------------------------------------------------------------------------

  5. class AR_2 extends ForecasterVec with NoFeatureSelectionF

    The AR_2 class provides basic time series analysis capabilities for Auto- Regressive 'AR'.

    The AR_2 class provides basic time series analysis capabilities for Auto- Regressive 'AR'. In an 'AR_2' model, 1 refers to the order of the Auto-Regressive components of the model. AR_2 models are often used for forecasting. Given time series data stored in vector 'y', its next value 'y_t = y(t)' may be predicted based on prior values of 'y' and its noise:

    y_t = c + Σ(φ_k y_t-k)

    where 'c' is a constant, 'φ' is the autoregressive coefficient vector, and 'e' is the noise vector. ----------------------------------------------------------------------------------

  6. class ELM_3L1_4TS extends ELM_3L1 with ForecasterMat

    The ELM_3L1_4TS class uses multiple regression to fit the lagged data.

    The ELM_3L1_4TS class uses multiple regression to fit the lagged data. Lag columns ranging from 'lag1' (inclusive) to 'lag2' (exclusive) are added before delegating the problem to the Regression class. A constant term for intercept can be added (@see 'allForms' method) but must not include intercept (column of ones) in initial data matrix.

  7. class ExpSmoothing extends ForecasterVec with NoFeatureSelectionF

    The ExpSmoothing class provide very basic time series analysis capabilities of Exponential Smoothing models.

    The ExpSmoothing class provide very basic time series analysis capabilities of Exponential Smoothing models. ExpSmoothing models are often used for forecasting. Given time series data stored in vector 'y', its next value 'y_t = y(t)' may be predicted based on prior/smoothed values of 'y':

    y_t = s_t-1 + α (s_t-1 - s_t-2)

    where vector 's' is the smoothed version of vector 'y' and 'α in [0, 1]' is the smoothing parameter. Trend and seasonality can be factored into the model with two additional smoothing parameters 'β' and 'γ', respectively.

  8. trait ForecasterMat extends AnyRef

    The ForecasterMat trait add the 'forecast' method to PredictorMat subclasses facilitating the adaption of predictive modeling techniques such as Regression for use in forecasting, viz.

    The ForecasterMat trait add the 'forecast' method to PredictorMat subclasses facilitating the adaption of predictive modeling techniques such as Regression for use in forecasting, viz. Regression4TS. Note: for forecasting, cross-validation needs to be of the rolling-validation form.

    See also

    the 'crossValidate' method in RollingValidation

  9. abstract class ForecasterVec extends Fit with Predictor

    The ForecasterVec abstract class provides a common framework for several forecasters.

    The ForecasterVec abstract class provides a common framework for several forecasters. Note, the 'train' method must be called first followed by 'eval'.

  10. class KPSS_Stationarity extends UnitRoot

    The KPSS class provides capabilities of performing KPSS test to determine if a time series is stationary around a deterministic trend.

    The KPSS class provides capabilities of performing KPSS test to determine if a time series is stationary around a deterministic trend. This code is translated from the C++ code found in

    See also

    github.com/olmallet81/URT.

  11. class KalmanFilter extends AnyRef

    The KalmanFilter class is used to fit state-space models.

    The KalmanFilter class is used to fit state-space models.

    x_t = F x_t-1 + G u_t + w_t (State Equation) z_t = H x_t + v_t (Observation/Measurement Equation)

  12. class MA extends ForecasterVec

    The MA class provides basic time series analysis capabilities for 'MA' models.

    The MA class provides basic time series analysis capabilities for 'MA' models. In an 'MA(q)' model, 'q' refers to the order of the Moving-Average component of the model. MA models are often used for forecasting. Given time series data stored in vector 'y', its next value 'y_t = y(t)' may be predicted based on the noise/residuals/shocks:

    y_t = c + Σ(θ_i e_t-i) + e_t

    where 'c' is a constant, 'θ' is the moving-average parameters vector, and 'e' is the noise vector. ------------------------------------------------------------------------------

  13. class MA_1 extends ForecasterVec with NoFeatureSelectionF

    The MA_1 class provides basic time series analysis capabilities for 'MA_1' models.

    The MA_1 class provides basic time series analysis capabilities for 'MA_1' models. In an 'MA_1' model, 1 refers to the order of the Moving-Average component of the model. MA_1 models are often used for forecasting. Given time series data stored in vector 'y', its next value 'y_t = y(t)' may be predicted based on the noise/residuals/shocks:

    y_t = c + θ e_t-i + e_t

    where 'c' is a constant, 'θ' is the moving-average parameters vector, and 'e' is the noise vector. ------------------------------------------------------------------------------

  14. class MovingAverage extends ForecasterVec with NoFeatureSelectionF

    The MovingAverage class provides basic time series analysis capabilities.

    The MovingAverage class provides basic time series analysis capabilities. For a 'MovingAverage' model with the time series data stored in vector 'y', the next value 'y_t = y(t)' may be predicted based on the mean of prior values of 'y' and its noise:

    y_t = mean (y_t-1, ..., y_t-q) + e_t

    where 'e' is the noise vector and 'q' is the number of prior values used to compute the mean.

  15. class MovingAverageD extends ForecasterVec with NoFeatureSelectionF

    The MovingAverageD class provides basic time series analysis capabilities.

    The MovingAverageD class provides basic time series analysis capabilities. For a 'MovingAverageD' model with the time series data stored in vector 'y', the next value 'y_t = y(t)' may be predicted based on the mean of prior values of 'y' and its noise:

    y_t = mean (y_t-1, ..., y_t-q) + e_t

    where 'e' is the noise vector and 'q' is the number of prior values used to compute the mean.

  16. class NeuralNet_3L1_4TS extends NeuralNet_3L1 with ForecasterMat

    The NeuralNet_3L1_4TS class uses a neural network to fit the lagged data.

    The NeuralNet_3L1_4TS class uses a neural network to fit the lagged data. Lag columns ranging from 'lag1' (inclusive) to 'lag2' (inclusive) are added before delegating the problem to the NeuralNet_3L1 class. A constant term for intercept can be added (@see 'allForms' method) but must not include intercept (column of ones) in initial data matrix.

  17. class NeuralNet_3L1_4TSy extends NeuralNet_3L1 with ForecasterMat

    The NeuralNet_3L1_4TSy class uses a neural network to fit the lagged data.

    The NeuralNet_3L1_4TSy class uses a neural network to fit the lagged data. Lag columns ranging from 'lag1' (inclusive) to 'lag2' (inclusive) are added before delegating the problem to the NeuralNet_3L1 class. A constant term for intercept can be added (@see 'allForms' method).

  18. trait NoFeatureSelectionF extends AnyRef

    The NoFeatureSelection trait is for modeling techniques that do not support feature selection (adding/remove variables from the model).

    The NoFeatureSelection trait is for modeling techniques that do not support feature selection (adding/remove variables from the model). For example, AR_1 only has one feature/predictor variable, so feature selection makes no sense for this modeling technique. Therefore the 'forwardSel' defined in ForecasterVec is set to throw an exception.

  19. class NullModel extends ForecasterVec with NoFeatureSelectionF

    The NullModel class provides basic time series analysis capabilities.

    The NullModel class provides basic time series analysis capabilities. For a 'NullModel' model with the time series data stored in vector 'y', the next value 'y_t = y(t)' may be predicted based on the prior value of 'y' and its noise:

    y_t = ym + e_t

    where 'e' is the noise vector. Random Walk is a special case of AR(1) with the parameter set to one. ------------------------------------------------------------------------------

  20. class QuadRegression4TS extends QuadRegression with ForecasterMat

    The QuadRegression4TS class uses multiple regression to fit the lagged data.

    The QuadRegression4TS class uses multiple regression to fit the lagged data. Lag columns ranging from 'lag1' (inclusive) to 'lag2' (exclusive) are added before delegating the problem to the Regression class. A constant term for intercept can be added (@see 'allForms' method) but must not include intercept (column of ones) in initial data matrix.

  21. class QuadRegression4TSy extends QuadRegression with ForecasterMat

    The QuadRegression4TSy class uses a neural network to fit the lagged data.

    The QuadRegression4TSy class uses a neural network to fit the lagged data. Lag columns ranging from 'lag1' (inclusive) to 'lag2' (inclusive) are added before delegating the problem to the QuadRegression class. A constant term for intercept can be added (@see 'allForms' method).

  22. class QuadSpline extends ForecasterVec with NoFeatureSelectionF

    The QuadSpline class fits quadratic splines to time-series data that are equally spaced in time.

    The QuadSpline class fits quadratic splines to time-series data that are equally spaced in time. A sliding window consisting of three data points is perfectly fit to a quadratic curve.

    y_t = a + bt + ct^2

    Note, slope matching and smoothness issues are ignored.

    See also

    wordsandbuttons.online/quadratic_splines_are_useful_too.html Any time point from t = 3 to the end of time series may be forecasted.

  23. class QuadXRegression4TS extends QuadXRegression with ForecasterMat

    The QuadXRegression4TS class uses multiple regression to fit the lagged data.

    The QuadXRegression4TS class uses multiple regression to fit the lagged data. Lag columns ranging from 'lag1' (inclusive) to 'lag2' (exclusive) are added before delegating the problem to the Regression class. A constant term for intercept can be added (@see 'allForms' method) but must not include intercept (column of ones) in initial data matrix.

  24. class QuadXRegression4TSy extends QuadXRegression with ForecasterMat

    The QuadXRegression4TSy class uses a neural network to fit the lagged data.

    The QuadXRegression4TSy class uses a neural network to fit the lagged data. Lag columns ranging from 'lag1' (inclusive) to 'lag2' (inclusive) are added before delegating the problem to the QuadXRegression class. A constant term for intercept can be added (@see 'allForms' method).

  25. class RandomWalk extends ForecasterVec with NoFeatureSelectionF

    The RandomWalk class provides basic time series analysis capabilities.

    The RandomWalk class provides basic time series analysis capabilities. For a 'RandomWalk' model with the time series data stored in vector 'y', the next value 'y_t = y(t)' may be predicted based on the prior value of 'y' and its noise:

    y_t = y_t-1 + e_t

    where 'e' is the noise vector. Random Walk is a special case of AR(1) with the parameter set to one. ------------------------------------------------------------------------------

  26. class Regression4TS extends Regression with ForecasterMat

    The Regression4TS class uses multiple regression to fit the lagged data.

    The Regression4TS class uses multiple regression to fit the lagged data. Lag columns ranging from 'lag1' (inclusive) to 'lag2' (inclusive) are added before delegating the problem to the Regression class. A constant term for intercept can be added (@see 'allForms' method) but must not include intercept (column of ones) in initial data matrix.

  27. class Regression4TSy extends Regression with ForecasterMat

    The Regression4TSy class uses multiple regression on the lagged response variable.

    The Regression4TSy class uses multiple regression on the lagged response variable. Lag columns ranging from 'lag1' (inclusive) to 'lag2' (inclusive) are added before delegating the problem to the Regression class. A constant term for intercept can be added (@see 'allForms' method).

    See also

    the ExampleEcon4 object.

  28. class SARIMA extends ARIMA

    The SARIMA class provides basic time series analysis capabilities for Auto- Regressive 'AR' Integrated 'I' Moving-Average 'MA' models.

    The SARIMA class provides basic time series analysis capabilities for Auto- Regressive 'AR' Integrated 'I' Moving-Average 'MA' models. In a 'SARIMA(p, d, q)' model, 'p' and 'q' refer to the order of the Auto-Regressive and Moving-Average components of the model; 'd' refers to the order of differencing. Given time series data stored in vector 'y', its next value 'y_t = y(t)' may be predicted based on prior values of 'y' and its noise:

    y_t = δ + Σ(φ_i y_t-i) + Σ(θ_i e_t-i) + e_t

    where 'δ' is a constant, 'φ' is the auto-regressive coefficient vector, 'θ' is the moving-average coefficient vector, and 'e' is the noise vector. ------------------------------------------------------------------------------ If 'd > 0', then the time series must be differenced first before applying the above model. Seasonal differencing, auto-regressive and moving average factors can be incorporated into the model by applying seasonal differencing (possibly in addition to simple differencing) first, then add the seasonal auto-regressive and moving average terms, that rely on lagged values and errors, respectively, from one or more seasonal periods in the past, on the right hand side of the equation. ------------------------------------------------------------------------------

  29. class SARIMAX extends ForecasterVec

    The SARIMAX class provides basic time series analysis capabilities for Auto- Regressive 'AR' Integrated 'I' Moving-Average 'MA' models.

    The SARIMAX class provides basic time series analysis capabilities for Auto- Regressive 'AR' Integrated 'I' Moving-Average 'MA' models. In an 'SARIMAX(p, d, q)' model, 'p' and 'q' refer to the order of the Auto-Regressive and Moving-Average components of the model; 'd' refers to the order of differencing. SARIMAX models are often used for forecasting. Given time series data stored in vector 'y', its next value 'y_t = y(t)' may be predicted based on prior values of 'y' and its noise:

    y_t = c + Σ(φ_i y_t-i) + Σ(θ_i e_t-i) + βχ + e_t

    where 'c' is a constant, 'φ' is the autoregressive coefficient vector, 'θ' is the moving-average coefficient vector, and 'e' is the noise vector. If 'd' > 0, then the time series must be differenced first before applying the above model. Seasonal differencing, autoregressive and moving average factors can be incorporated into the model by applying seasonal differencing (possibly in addition to simple differencing) first, then add the seasonal autoregressive and moving average terms, that rely on lagged values and errors, respectively, from one or more seasonal periods in the past, on the right hand side of the equation. Exogenous/External regressor may also be added to the right-hand size of the model in a similar manner to Regression models. ------------------------------------------------------------------------------

  30. class SimpleExpSmoothing extends ForecasterVec with NoFeatureSelectionF

    The SimpleExpSmoothing class provide very basic time series analysis using Simple Exponential Smoothing models.

    The SimpleExpSmoothing class provide very basic time series analysis using Simple Exponential Smoothing models. The forecasted value is the weighted average the latest value and the previous smoothed value. The smoothing parameter 'α in [0, 1]' causes the contributions of older values to decay exponentially.

    See also

    Smoothing Equation in section 7.1. s_t = α y_t-1 + (1 - α) s_t-1 // smoothing equation yf_t = s_t // forecast equation where vector 's' is the smoothed version of vector 'y'.

  31. case class Stats(y_: VectoD, lags: Int) extends Product with Serializable

    The Stats case class is used to hold basic statistical information: mean, variance, auto-covariance, and auto-correlation.

    The Stats case class is used to hold basic statistical information: mean, variance, auto-covariance, and auto-correlation. Note gamma0 (biased) does not equal the sample variance (unbiased)

    y_

    the response vector (time-series data) for the training/full dataset

    lags

    the maximum number of lags

  32. abstract class UnitRoot extends AnyRef

    The UnitRoot abstract class provides a common framework for various unit root testers for Time Series Stationarity.

    The UnitRoot abstract class provides a common framework for various unit root testers for Time Series Stationarity. This code is translated from the C++ code found in

    See also

    github.com/olmallet81/URT.

Value Members

  1. val BASE_DIR: String

    The relative path for base directory

  2. def diff(y: VectoD, d: Int): Unit

    Take the 'd'-th difference on vector/time-series 'y'.

    Take the 'd'-th difference on vector/time-series 'y'. For efficiency, this method is destructive of 'y' (make a copy to preserve).

    y

    the vector/time-series to be differenced

    d

    the order or number of differences to be taken

  3. def diff(y: VectoD): Unit

    Take the '1'-st difference on vector/time-series 'y'.

    Take the '1'-st difference on vector/time-series 'y'. For efficiency, this method is destructive of 'y' (make a copy to preserve). Note, it stores the first value in the original times-series in the first position of the differenced vector.

    y

    the vector/time-series to be differenced

  4. def diffinv(y: VectoD, d: Int): Unit

    Take the 'd'-th inverse-difference on vector/time-series 'y'.

    Take the 'd'-th inverse-difference on vector/time-series 'y'. For efficiency, this method is destructive of 'y' (make a copy to preserve). Restores the original time-series if 'y(0)' holds first value in original time-series.

    y

    the vector/time-series to be inverse-differenced

    d

    the order or number of inverse-differences to be taken

  5. def diffinv(y: VectoD): Unit

    Take the '1'-st inverse-difference on vector/time-series 'y'.

    Take the '1'-st inverse-difference on vector/time-series 'y'. For efficiency, this method is destructive of 'y' (make a copy to preserve). Restores the original time-series if 'y(0)' holds first value in original time-series.

    y

    the vector/time-series to be inverse-differenced

  6. final def flaw(method: String, message: String): Unit
    Definition Classes
    Error
  7. def sdiff(y: VectoD, d: Int, s: Int): Unit

    Take the 'd'-th seasonal difference on vector/time-series 'y'.

    Take the 'd'-th seasonal difference on vector/time-series 'y'. For efficiency, this method is destructive of 'y' (make a copy to preserve).

    y

    the vector/time-series to be differenced

    d

    the order or number of differences to be taken

    s

    the seasonal period

  8. def sdiff(y: VectoD, s: Int): Unit

    Take the '1'-st seasonal difference on vector/time-series 'y'.

    Take the '1'-st seasonal difference on vector/time-series 'y'. For efficiency, this method is destructive of 'y' (make a copy to preserve).

    y

    the vector/time-series to be differenced

    s

    the seasonal period

  9. def sdiffinv(y: VectoD, d: Int, s: Int): Unit

    Take the 'd'-th seasonal inverse-difference on vector/time-series 'y'.

    Take the 'd'-th seasonal inverse-difference on vector/time-series 'y'. For efficiency, this method is destructive of 'y' (make a copy to preserve). Restores the original time-series if 'y(0)' holds first value in original time-series.

    y

    the vector/time-series to be inverse-differenced

    d

    the order or number of inverse-differences to be taken

    s

    the seasonal period

  10. def sdiffinv(y: VectoD, s: Int): Unit

    Take the '1'-st seasonal inverse-difference on vector/time-series 'y'.

    Take the '1'-st seasonal inverse-difference on vector/time-series 'y'. For efficiency, this method is destructive of 'y' (make a copy to preserve). Restores the original time-series if 'y(0)' holds first value in original time-series.

    y

    the vector/time-series to be inverse-differenced

    s

    the seasonal period

  11. object AR

    The AR companion object provides factory methods for the AR class.

  12. object ARIMA

    Companion object for class ARIMA.

    Companion object for class ARIMA. Includes features related to differencing and automated order selection.

    See also

    www.jstatsoft.org/article/view/v027i03/v27i03.pdf

  13. object ARIMATest extends App

    The ARIMATest object is used to test the ARIMA class.

    The ARIMATest object is used to test the ARIMA class. > runMain scalation.analytics.forcaster.ARIMATest

  14. object ARIMATest2 extends App

    The ARIMATest2 object is used to test the ARIMA class.

    The ARIMATest2 object is used to test the ARIMA class. > runMain scalation.analytics.forecaster.ARIMATest2

  15. object ARIMATest3 extends App

    The ARIMATest3 object is used to test the ARIMA class on real data: Forecasting lake levels.

    The ARIMATest3 object is used to test the ARIMA class on real data: Forecasting lake levels.

    See also

    cran.r-project.org/web/packages/fpp/fpp.pdf > runMain scalation.analytics.forecaster.ARIMATest3

  16. object ARIMATest4 extends App

    The ARIMATest4 object is used to test the ARIMA class.

    The ARIMATest4 object is used to test the ARIMA class. > runMain scalation.analytics.forecaster.ARIMATest4

  17. object ARIMATest5 extends App

    The ARIMATest5 object is used to test the ARIMA class.

    The ARIMATest5 object is used to test the ARIMA class. > runMain scalation.analytics.forecaster.ARIMATest5

  18. object ARIMATest6 extends App

    The ARIMATest6 object is used to test the ARIMA class on real data: Forecasting lake levels.

    The ARIMATest6 object is used to test the ARIMA class on real data: Forecasting lake levels. Select the best number of lags.

    See also

    cran.r-project.org/web/packages/fpp/fpp.pdf > runMain scalation.analytics.forecaster.ARIMATest6

  19. object ARMA

    The ARMA companion object maintains values for hyper-parameters.

  20. object ARMATest extends App

    The ARMATest object is used to test the ARMA class.

    The ARMATest object is used to test the ARMA class. > runMain scalation.analytics.forecaster.ARMATest

  21. object ARMATest2 extends App

    The ARMATest2 object is used to test the ARMA class.

    The ARMATest2 object is used to test the ARMA class. > runMain scalation.analytics.forecaster.ARMATest2

  22. object ARMATest3 extends App

    The ARMATest3 object is used to test the ARMA class on real data: Forecasting lake levels.

    The ARMATest3 object is used to test the ARMA class on real data: Forecasting lake levels.

    See also

    cran.r-project.org/web/packages/fpp/fpp.pdf > runMain scalation.analytics.forecaster.ARMATest3

  23. object ARMATest4 extends App

    The ARMATest4 object is used to test the ARMA class.

    The ARMATest4 object is used to test the ARMA class. > runMain scalation.analytics.forecaster.ARMATest4

  24. object ARMATest5 extends App

    The ARMATest5 object is used to test the ARMA class.

    The ARMATest5 object is used to test the ARMA class. > runMain scalation.analytics.forecaster.ARMATest5

  25. object ARMATest6 extends App

    The ARMATest6 object is used to test the ARMA class on real data: Forecasting lake levels.

    The ARMATest6 object is used to test the ARMA class on real data: Forecasting lake levels. Select the best number of lags.

    See also

    cran.r-project.org/web/packages/fpp/fpp.pdf > runMain scalation.analytics.forecaster.ARMATest6

  26. object ARTest extends App

    The ARTest object is used to test the AR class on simulated data with uniformly distributed errors.

    The ARTest object is used to test the AR class on simulated data with uniformly distributed errors. > runMain scalation.analytics.forecaster.ARTest

  27. object ARTest2 extends App

    The ARTest2 object is used to test the AR class on simulated data with normally distributed errors.

    The ARTest2 object is used to test the AR class on simulated data with normally distributed errors. The order 'p' hyper-parameter should be re-assigned (try 1, 2, 3, ...). > runMain scalation.analytics.forecaster.ARTest2

  28. object ARTest3 extends App

    The ARTest3 object is used to test the AR class on real data: Forecasting lake levels.

    The ARTest3 object is used to test the AR class on real data: Forecasting lake levels.

    See also

    cran.r-project.org/web/packages/fpp/fpp.pdf > runMain scalation.analytics.forecaster.ARTest3

  29. object ARTest4 extends App

    The ARTest4 object is used to test the AR class on real data: Forecasting traffic flow.

    The ARTest4 object is used to test the AR class on real data: Forecasting traffic flow. The order hyper-parameter 'p' should be reassigned (try 1, 2, 3, ...). > runMain scalation.analytics.forecaster.ARTest4

  30. object ARTest5 extends App

    The ARTest5 object is used to test the AR class on simulated data with normally distributed errors and the response exhibits a quadratic trend.

    The ARTest5 object is used to test the AR class on simulated data with normally distributed errors and the response exhibits a quadratic trend. Note: should have p <= n / 10 for reliable QoF results from ScalaTion (FIX). > runMain scalation.analytics.forecaster.ARTest5

  31. object ARTest6 extends App

    The ARTest6 object is used to test the AR class on real data: Forecasting lake levels.

    The ARTest6 object is used to test the AR class on real data: Forecasting lake levels. Performs cross-validation.

    See also

    cran.r-project.org/web/packages/fpp/fpp.pdf > runMain scalation.analytics.forecaster.ARTest6

  32. object ARTest7 extends App

    The ARTest7 object is used to test the AR class on real data: Generate an AR(2) process that results in rho1 being 0.

    The ARTest7 object is used to test the AR class on real data: Generate an AR(2) process that results in rho1 being 0.

    See also

    cran.r-project.org/web/packages/fpp/fpp.pdf > runMain scalation.analytics.forecaster.ARTest7

  33. object ARTest8 extends App

    The ARTest8 object is used to test the AR class on real data: Forecasting lake levels.

    The ARTest8 object is used to test the AR class on real data: Forecasting lake levels. Select the best number of lags.

    See also

    cran.r-project.org/web/packages/fpp/fpp.pdf > runMain scalation.analytics.forecaster.ARTest8

  34. object AR_1

    The AR_1 companion object provides factory methods for the AR_1 class.

  35. object AR_1Test extends App

    The AR_1Test object is used to test the AR_1 class on simulated data with uniformly distributed errors.

    The AR_1Test object is used to test the AR_1 class on simulated data with uniformly distributed errors. > runMain scalation.analytics.forecaster.AR_1Test

  36. object AR_1Test2 extends App

    The AR_1Test2 object is used to test the AR_1 class on simulated data with normally distributed errors.

    The AR_1Test2 object is used to test the AR_1 class on simulated data with normally distributed errors. > runMain scalation.analytics.forecaster.AR_1Test2

  37. object AR_1Test3 extends App

    The AR_1Test3 object is used to test the AR_1 class on real data: Forecasting lake levels.

    The AR_1Test3 object is used to test the AR_1 class on real data: Forecasting lake levels.

    See also

    cran.r-project.org/web/packages/fpp/fpp.pdf > runMain scalation.analytics.forecaster.AR_1Test3

  38. object AR_1Test4 extends App

    The AR_1Test4 object is used to test the AR_1 class on real data: Forecasting traffic flow.

    The AR_1Test4 object is used to test the AR_1 class on real data: Forecasting traffic flow. > runMain scalation.analytics.forecaster.AR_1Test4

  39. object AR_1Test5 extends App

    The AR_1Test5 object is used to test the AR_1 class on simulated data with normally distributed errors and the response exhibits a quadratic trend.

    The AR_1Test5 object is used to test the AR_1 class on simulated data with normally distributed errors and the response exhibits a quadratic trend. > runMain scalation.analytics.forecaster.AR_1Test5

  40. object AR_1Test6 extends App

    The AR_1Test6 object is used to test the AR_1 class on real data: Forecasting lake levels.

    The AR_1Test6 object is used to test the AR_1 class on real data: Forecasting lake levels. Performs cross-validation.

    See also

    cran.r-project.org/web/packages/fpp/fpp.pdf > runMain scalation.analytics.forecaster.AR_1Test6

  41. object AR_2

    The AR_2 companion object provides factory methods for the AR_2 class.

  42. object AR_2Test extends App

    The AR_2Test object is used to test the AR_2 class on simulated data with uniformly distributed errors.

    The AR_2Test object is used to test the AR_2 class on simulated data with uniformly distributed errors. > runMain scalation.analytics.forecaster.AR_2Test

  43. object AR_2Test2 extends App

    The AR_2Test2 object is used to test the AR_2 class on simulated data with normally distributed errors.

    The AR_2Test2 object is used to test the AR_2 class on simulated data with normally distributed errors. > runMain scalation.analytics.forecaster.AR_2Test2

  44. object AR_2Test3 extends App

    The AR_2Test3 object is used to test the AR_2 class on real data: Forecasting lake levels.

    The AR_2Test3 object is used to test the AR_2 class on real data: Forecasting lake levels.

    See also

    cran.r-project.org/web/packages/fpp/fpp.pdf > runMain scalation.analytics.forecaster.AR_2Test3

  45. object AR_2Test4 extends App

    The AR_2Test4 object is used to test the AR_2 class on real data: Forecasting traffic flow.

    The AR_2Test4 object is used to test the AR_2 class on real data: Forecasting traffic flow. > runMain scalation.analytics.forecaster.AR_2Test4

  46. object AR_2Test5 extends App

    The AR_2Test5 object is used to test the AR_2 class on simulated data with normally distributed errors and the response exhibits a quadratic trend.

    The AR_2Test5 object is used to test the AR_2 class on simulated data with normally distributed errors and the response exhibits a quadratic trend. > runMain scalation.analytics.forecaster.AR_2Test5

  47. object AR_2Test6 extends App

    The AR_2Test6 object is used to test the AR_2 class on real data: Forecasting lake levels.

    The AR_2Test6 object is used to test the AR_2 class on real data: Forecasting lake levels. Performs cross-validation.

    See also

    cran.r-project.org/web/packages/fpp/fpp.pdf > runMain scalation.analytics.forecaster.AR_2Test6

  48. object DiffTest extends App

    The DiffTest is used to test the 'diff' and 'diffinv' differencing functions in the forecaster package object using 'square (k)'.

    The DiffTest is used to test the 'diff' and 'diffinv' differencing functions in the forecaster package object using 'square (k)'. > runMain scalation.analytics.forecaster.DiffTest

  49. object DiffTest2 extends App

    The DiffTest2 is used to test the 'diff' and 'diffinv' differencing functions in the forecaster package object using Fibonacci Numbers 'fib(k)'.

    The DiffTest2 is used to test the 'diff' and 'diffinv' differencing functions in the forecaster package object using Fibonacci Numbers 'fib(k)'. > runMain scalation.analytics.forecaster.DiffTest2

  50. object DiffTest3 extends App

    The DiffTest3 is used to test the 'sdiff' and 'sdiffinv' seasonal differencing functions in the 'forecaster' package object using 'square (k)'.

    The DiffTest3 is used to test the 'sdiff' and 'sdiffinv' seasonal differencing functions in the 'forecaster' package object using 'square (k)'. > runMain scalation.analytics.forecaster.DiffTest3

  51. object ELM_3L1_4TS extends ModelFactory

    The ELM_3L1_4TS companion object provides factory functions and functions for creating functional forms.

  52. object ELM_3L1_4TSTest extends App

    The ELM_3L1_4TSTest object is used to test the ELM_3L1_4TS class.

    The ELM_3L1_4TSTest object is used to test the ELM_3L1_4TS class. > runMain scalation.analytics.forecaster.ELM_3L1_4TSTest

  53. object ELM_3L1_4TSTest2 extends App

    The ELM_3L1_4TSTest2 object is used to test the ELM_3L1_4TS class.

    The ELM_3L1_4TSTest2 object is used to test the ELM_3L1_4TS class. The 'x' matrix in one dimensional. > runMain scalation.analytics.forecaster.ELM_3L1_4TSTest2

  54. object ELM_3L1_4TSTest3 extends App

    The ELM_3L1_4TSTest3 object is used to test the ELM_3L1_4TS class.

    The ELM_3L1_4TSTest3 object is used to test the ELM_3L1_4TS class. The 'x' matrix in two dimensional. > runMain scalation.analytics.forecaster.ELM_3L1_4TSTest3

  55. object ELM_3L1_4TSTest4 extends App

    The ELM_3L1_4TSTest4 object tests the ELM_3L1_4TS class using the AutoMPG dataset.

    The ELM_3L1_4TSTest4 object tests the ELM_3L1_4TS class using the AutoMPG dataset. It illustrates using the Relation class for reading the data from a .csv file "auto-mpg.csv". Assumes no missing values. It also combines feature selection with cross-validation and plots R2, R2 Bar and R^2 cv vs. the instance index. > runMain scalation.analytics.forecaster.ELM_3L1_4TSTest4

  56. object ELM_3L1_4TSTest5 extends App

    The ELM_3L1_4TSTest5 object tests ELM_3L1_4TS class using the following regression equation.

    The ELM_3L1_4TSTest5 object tests ELM_3L1_4TS class using the following regression equation.

    y = b dot x = b_0 + b_1*x1 + b_2*x_2.

    > runMain scalation.analytics.forecaster.ELM_3L1_4TSTest5

  57. object ExampleEcon extends App

    The ExampleEcon object tests various prediction and forecasting techniques on daily economic/finance time-series data.

    The ExampleEcon object tests various prediction and forecasting techniques on daily economic/finance time-series data. > runMain scalation.analytics.forecaster.ExampleEcon

  58. object ExampleEcon2 extends App

    The ExampleEcon2 object tests various prediction and forecasting techniques on daily economic/finance time-series data.

    The ExampleEcon2 object tests various prediction and forecasting techniques on daily economic/finance time-series data. > runMain scalation.analytics.forecaster.ExampleEcon2

  59. object ExampleEcon2_SARIMA extends App

    The ExampleEcon2_SARIMA object tests various prediction and forecasting techniques on daily economic/finance time-series data.

    The ExampleEcon2_SARIMA object tests various prediction and forecasting techniques on daily economic/finance time-series data. > runMain scalation.analytics.forecaster.ExampleEcon2_SARIMA

  60. object ExampleEcon3 extends App

    The ExampleEcon3 object tests various prediction and forecasting techniques on daily economic/finance time-series data.

    The ExampleEcon3 object tests various prediction and forecasting techniques on daily economic/finance time-series data. > runMain scalation.analytics.forecaster.ExampleEcon3

  61. object ExampleEcon4 extends App

    The ExampleEcon4 object tests various prediction and forecasting techniques on daily economic/finance time-series data.

    The ExampleEcon4 object tests various prediction and forecasting techniques on daily economic/finance time-series data. > runMain scalation.analytics.forecaster.ExampleEcon4

  62. object ExpSmoothing

    The ExpSmoothing companion object provides factory methods for the ExpSmoothing class.

  63. object ExpSmoothingTest extends App

    The ExpSmoothingTest object is used to test the ExpSmoothing class.

    The ExpSmoothingTest object is used to test the ExpSmoothing class. > runMain scalation.analytics.forecaster.ExpSmoothingTest

  64. object ForecasterVec

    The ForecasterVec companion object provides functions useful for forecasting models.

    The ForecasterVec companion object provides functions useful for forecasting models. It also contains a sample dataset.

  65. object ForecasterVecTest extends App

    The ForecasterVecTest object tests the ForecasterVec companion object.

    The ForecasterVecTest object tests the ForecasterVec companion object. > runMain scalation.analytics.forecaster.ForecasterVecTest

  66. object ForecasterVecTest2 extends App

    The ForecasterVecTest2 object tests the ForecasterVec companion object and models/classes that extend ForecasterVec.

    The ForecasterVecTest2 object tests the ForecasterVec companion object and models/classes that extend ForecasterVec. > runMain scalation.analytics.forecaster.ForecasterVecTest2

  67. object KPSS_Stationarity

    The companion object for KPSS class, containing critical value coefficients needed in KPSS tests for Time Series Stationarity around a deterministic trend.

  68. object KPSS_StationarityTest extends App

    The KPSS_StationarityTest object is used to test the KPSS_Stationarity class.

    The KPSS_StationarityTest object is used to test the KPSS_Stationarity class. > runMain scalation.analytics.forecaster.KPSS_StationarityTest

  69. object KalmanFilterTest extends App

    The KalmanFilterTest object is used to test the KalmanFilter class.

    The KalmanFilterTest object is used to test the KalmanFilter class.

    See also

    en.wikipedia.org/wiki/Kalman_filter > runMain scalation.analytics.forecaster.KalmanFilterTest

  70. object MA

    The MA companion object provides factory methods for the MA class.

  71. object MATest extends App

    The MATest object is used to test the MA class.

    The MATest object is used to test the MA class. > runMain scalation.analytics.forecaster.MATest

  72. object MATest2 extends App

    The MATest2 object is used to test the MA class.

    The MATest2 object is used to test the MA class. > runMain scalation.analytics.forecaster.MATest2

  73. object MATest3 extends App

    The MATest3 object is used to test the MA class.

    The MATest3 object is used to test the MA class. Forecasting lake levels.

    See also

    cran.r-project.org/web/packages/fpp/fpp.pdf > runMain scalation.analytics.forecaster.MATest3

  74. object MATest4 extends App

    The MATest4 object is used to test the MA class.

    The MATest4 object is used to test the MA class. Forecasting traffic flow. > runMain scalation.analytics.forecaster.MATest4

  75. object MATest5 extends App

    The MATest5 object is used to test the MA class.

    The MATest5 object is used to test the MA class. > runMain scalation.analytics.forecaster.MATest5

  76. object MATest6 extends App

    The MATest6 object is used to test the MA class on real data: Forecasting lake levels.

    The MATest6 object is used to test the MA class on real data: Forecasting lake levels. Select the best number of lags.

    See also

    cran.r-project.org/web/packages/fpp/fpp.pdf > runMain scalation.analytics.forecaster.MATest6

  77. object MA_1

    The MA_1 companion object provides factory methods for the MA_1 class.

  78. object MA_1Test extends App

    The MA_1Test object is used to test the MA_1 class.

    The MA_1Test object is used to test the MA_1 class.

    y_t = c + θ e_{t-1} + e_t

    > runMain scalation.analytics.forecaster.MA_1Test

  79. object MA_1Test2 extends App

    The MA_1Test2 object is used to test the MA_1 class on simulated data with normally distributed errors.

    The MA_1Test2 object is used to test the MA_1 class on simulated data with normally distributed errors. > runMain scalation.analytics.forecaster.MA_1Test2

  80. object MA_1Test3 extends App

    The MA_1Test3 object is used to test the MA_1 class on real data: Forecasting lake levels.

    The MA_1Test3 object is used to test the MA_1 class on real data: Forecasting lake levels.

    See also

    cran.r-project.org/web/packages/fpp/fpp.pdf > runMain scalation.analytics.forecaster.MA_1Test3

  81. object MovingAverage

    The MovingAverage companion object provides factory methods for the MovingAverage class.

  82. object MovingAverageD

    The MovingAverageD companion object provides factory methods for the MovingAverageD class.

  83. object MovingAverageDTest extends App

    The MovingAverageDTest object is used to test the MovingAverageD class.

    The MovingAverageDTest object is used to test the MovingAverageD class. > runMain scalation.analytics.forecaster.MovingAverageDTest

  84. object MovingAverageDTest2 extends App

    The MovingAverageDTest2 object is used to test the MovingAverageD class.

    The MovingAverageDTest2 object is used to test the MovingAverageD class. > runMain scalation.analytics.forecaster.MovingAverageDTest2

  85. object MovingAverageDTest3 extends App

    The MovingAverageDTest3 object is used to test the MovingAverageD class.

    The MovingAverageDTest3 object is used to test the MovingAverageD class. Forecasting lake levels.

    See also

    cran.r-project.org/web/packages/fpp/fpp.pdf > runMain scalation.analytics.forecaster.MovingAverageDTest3

  86. object MovingAverageDTest4 extends App

    The MovingAverageDTest4 object is used to test the MovingAverageD class.

    The MovingAverageDTest4 object is used to test the MovingAverageD class. > runMain scalation.analytics.forecaster.MovingAverageDTest4

  87. object MovingAverageDTest5 extends App

    The MovingAverageDTest5 object is used to test the MovingAverageD class.

    The MovingAverageDTest5 object is used to test the MovingAverageD class. > runMain scalation.analytics.forecaster.MovingAverageDTest5

  88. object MovingAverageTest extends App

    The MovingAverageTest object is used to test the MovingAverage class.

    The MovingAverageTest object is used to test the MovingAverage class. > runMain scalation.analytics.forecaster.MovingAverageTest

  89. object MovingAverageTest2 extends App

    The MovingAverageTest2 object is used to test the MovingAverage class.

    The MovingAverageTest2 object is used to test the MovingAverage class. > runMain scalation.analytics.forecaster.MovingAverageTest2

  90. object MovingAverageTest3 extends App

    The MovingAverageTest3 object is used to test the MovingAverage class.

    The MovingAverageTest3 object is used to test the MovingAverage class. Forecasting lake levels.

    See also

    cran.r-project.org/web/packages/fpp/fpp.pdf > runMain scalation.analytics.forecaster.MovingAverageTest3

  91. object MovingAverageTest4 extends App

    The MovingAverageTest4 object is used to test the MovingAverage class.

    The MovingAverageTest4 object is used to test the MovingAverage class. > runMain scalation.analytics.forecaster.MovingAverageTest4

  92. object MovingAverageTest5 extends App

    The MovingAverageTest5 object is used to test the MovingAverage class.

    The MovingAverageTest5 object is used to test the MovingAverage class. > runMain scalation.analytics.forecaster.MovingAverageTest5

  93. object NeuralNet_3L1_4TS extends ModelFactory

    The NeuralNet_3L1_4TS companion object provides factory functions and functions for creating functional forms.

  94. object NeuralNet_3L1_4TSTest extends App

    The NeuralNet_3L1_4TSTest object is used to test the NeuralNet_3L1_4TS class.

    The NeuralNet_3L1_4TSTest object is used to test the NeuralNet_3L1_4TS class. > runMain scalation.analytics.forecaster.NeuralNet_3L1_4TSTest

  95. object NeuralNet_3L1_4TSTest2 extends App

    The NeuralNet_3L1_4TSTest2 object is used to test the NeuralNet_3L1_4TS class.

    The NeuralNet_3L1_4TSTest2 object is used to test the NeuralNet_3L1_4TS class. The 'x' matrix in one dimensional. > runMain scalation.analytics.forecaster.NeuralNet_3L1_4TSTest2

  96. object NeuralNet_3L1_4TSTest3 extends App

    The NeuralNet_3L1_4TSTest3 object is used to test the NeuralNet_3L1_4TS class.

    The NeuralNet_3L1_4TSTest3 object is used to test the NeuralNet_3L1_4TS class. The 'x' matrix in two dimensional. > runMain scalation.analytics.forecaster.NeuralNet_3L1_4TSTest3

  97. object NeuralNet_3L1_4TSTest4 extends App

    The NeuralNet_3L1_4TSTest4 object tests the NeuralNet_3L1_4TS class using the AutoMPG dataset.

    The NeuralNet_3L1_4TSTest4 object tests the NeuralNet_3L1_4TS class using the AutoMPG dataset. It illustrates using the Relation class for reading the data from a .csv file "auto-mpg.csv". Assumes no missing values. It also combines feature selection with cross-validation and plots R2, R2 Bar and R^2 cv vs. the instance index. > runMain scalation.analytics.forecaster.NeuralNet_3L1_4TSTest4

  98. object NeuralNet_3L1_4TSTest5 extends App

    The NeuralNet_3L1_4TSTest5 object tests NeuralNet_3L1_4TS class using the following regression equation.

    The NeuralNet_3L1_4TSTest5 object tests NeuralNet_3L1_4TS class using the following regression equation.

    y = b dot x = b_0 + b_1*x1 + b_2*x_2.

    > runMain scalation.analytics.forecaster.NeuralNet_3L1_4TSTest5

  99. object NeuralNet_3L1_4TSy extends ModelFactory

    The NeuralNet_3L1_4TSy companion object provides factory functions and functions for creating functional forms.

  100. object NeuralNet_3L1_4TSyTest extends App

    The NeuralNet_3L1_4TSyTest object is used to test the NeuralNet_3L1_4TSy class.

    The NeuralNet_3L1_4TSyTest object is used to test the NeuralNet_3L1_4TSy class. > runMain scalation.analytics.forecaster.NeuralNet_3L1_4TSTest

  101. object NeuralNet_3L1_4TSyTest2 extends App

    The NeuralNet_3L1_4TSyTest2 object is used to test the NeuralNet_3L1_4TSy class.

    The NeuralNet_3L1_4TSyTest2 object is used to test the NeuralNet_3L1_4TSy class. The order 'p' hyper-parameter should be re-assigned (try 1, 2, 3, ...). > runMain scalation.analytics.forecaster.NeuralNet_3L1_4TSyTest2

  102. object NeuralNet_3L1_4TSyTest3 extends App

    The NeuralNet_3L1_4TSyTest3 object is used to test the NeuralNet_3L1_4TSy class.

    The NeuralNet_3L1_4TSyTest3 object is used to test the NeuralNet_3L1_4TSy class. Forecasting lake levels.

    See also

    cran.r-project.org/web/packages/fpp/fpp.pdf > runMain scalation.analytics.forecaster.NeuralNet_3L1_4TSyTest3

  103. object NullModel

    The NullModel companion object provides factory methods for the NullModel class.

  104. object NullModelTest extends App

    The NullModelTest object is used to test the NullModel class.

    The NullModelTest object is used to test the NullModel class. > runMain scalation.analytics.forecaster.NullModelTest

  105. object NullModelTest2 extends App

    The NullModelTest2 object is used to test the NullModel class.

    The NullModelTest2 object is used to test the NullModel class. The order 'p' hyper-parameter is re-assigned. > runMain scalation.analytics.forecaster.NullModelTest2

  106. object NullModelTest3 extends App

    The NullModelTest3 object is used to test the NullModel class.

    The NullModelTest3 object is used to test the NullModel class. Forecasting lake levels.

    See also

    cran.r-project.org/web/packages/fpp/fpp.pdf > runMain scalation.analytics.forecaster.NullModelTest3

  107. object NullModelTest4 extends App

    The NullModelTest4 object is used to test the NullModel class.

    The NullModelTest4 object is used to test the NullModel class. > runMain scalation.analytics.forecaster.NullModelTest4

  108. object NullModelTest5 extends App

    The NullModelTest5 object is used to test the NullModel class.

    The NullModelTest5 object is used to test the NullModel class. > runMain scalation.analytics.forecaster.NullModelTest5

  109. object QuadRegression4TS extends ModelFactory

    The QuadRegression4TS companion object provides factory functions and functions for creating functional forms.

  110. object QuadRegression4TSTest extends App

    The QuadRegression4TSTest object is used to test the QuadRegression4TS class.

    The QuadRegression4TSTest object is used to test the QuadRegression4TS class. > runMain scalation.analytics.forecaster.QuadRegression4TSTest

  111. object QuadRegression4TSTest2 extends App

    The QuadRegression4TSTest2 object is used to test the QuadRegression4TS class.

    The QuadRegression4TSTest2 object is used to test the QuadRegression4TS class. The 'x' matrix in one dimensional. > runMain scalation.analytics.forecaster.QuadRegression4TSTest2

  112. object QuadRegression4TSTest3 extends App

    The QuadRegression4TSTest3 object is used to test the QuadRegression4TS class.

    The QuadRegression4TSTest3 object is used to test the QuadRegression4TS class. The 'x' matrix in two dimensional. > runMain scalation.analytics.forecaster.QuadRegression4TSTest3

  113. object QuadRegression4TSTest4 extends App

    The QuadRegression4TSTest4 object tests the QuadRegression4TS class using the AutoMPG dataset.

    The QuadRegression4TSTest4 object tests the QuadRegression4TS class using the AutoMPG dataset. It illustrates using the Relation class for reading the data from a .csv file "auto-mpg.csv". Assumes no missing values. It also combines feature selection with cross-validation and plots R2, R2 Bar and R^2 cv vs. the instance index. > runMain scalation.analytics.forecaster.QuadRegression4TSTest4

  114. object QuadRegression4TSTest5 extends App

    The QuadRegression4TSTest5 object tests QuadRegression4TS class using the following regression equation.

    The QuadRegression4TSTest5 object tests QuadRegression4TS class using the following regression equation.

    y = b dot x = b_0 + b_1*x1 + b_2*x_2.

    > runMain scalation.analytics.forecaster.QuadRegression4TSTest5

  115. object QuadRegression4TSy extends ModelFactory

    The QuadRegression4TSy companion object provides factory functions and functions for creating functional forms.

  116. object QuadRegression4TSyTest extends App

    The QuadRegression4TSyTest object is used to test the QuadRegression4TSy class.

    The QuadRegression4TSyTest object is used to test the QuadRegression4TSy class. > runMain scalation.analytics.forecaster.QuadRegression4TSyTest

  117. object QuadRegression4TSyTest2 extends App

    The QuadRegression4TSyTest2 object is used to test the QuadRegression4TSy class.

    The QuadRegression4TSyTest2 object is used to test the QuadRegression4TSy class. The order 'p' hyper-parameter should be re-assigned (try 1, 2, 3, ...). > runMain scalation.analytics.forecaster.QuadRegression4TSyTest2

  118. object QuadRegression4TSyTest3 extends App

    The QuadRegression4TSyTest3 object is used to test the QuadRegression4TSy class.

    The QuadRegression4TSyTest3 object is used to test the QuadRegression4TSy class. Forecasting lake levels.

    See also

    cran.r-project.org/web/packages/fpp/fpp.pdf > runMain scalation.analytics.forecaster.QuadRegression4TSyTest3

  119. object QuadSpline

    The QuadSpline companion object provides factory methods for the QuadSpline class.

  120. object QuadSplineTest extends App

    The QuadSplineTest object is used to test the QuadSpline class.

    The QuadSplineTest object is used to test the QuadSpline class. Forecasting Fibonacci numbers. > runMain scalation.analytics.forecaster.QuadSplineTest

  121. object QuadSplineTest2 extends App

    The QuadSplineTest2 object is used to test the QuadSpline class.

    The QuadSplineTest2 object is used to test the QuadSpline class. > runMain scalation.analytics.forecaster.QuadSplineTest2

  122. object QuadSplineTest3 extends App

    The QuadSplineTest3 object is used to test the QuadSpline class.

    The QuadSplineTest3 object is used to test the QuadSpline class. Forecasting lake levels.

    See also

    cran.r-project.org/web/packages/fpp/fpp.pdf > runMain scalation.analytics.forecaster.QuadSplineTest3

  123. object QuadXRegression4TS extends ModelFactory

    The QuadXRegression4TS companion object provides factory functions and functions for creating functional forms.

  124. object QuadXRegression4TSTest extends App

    The QuadXRegression4TSTest object is used to test the QuadXRegression4TS class.

    The QuadXRegression4TSTest object is used to test the QuadXRegression4TS class. > runMain scalation.analytics.forecaster.QuadXRegression4TSTest

  125. object QuadXRegression4TSTest2 extends App

    The QuadXRegression4TSTest2 object is used to test the QuadXRegression4TS class.

    The QuadXRegression4TSTest2 object is used to test the QuadXRegression4TS class. The 'x' matrix in one dimensional. > runMain scalation.analytics.forecaster.QuadXRegression4TSTest2

  126. object QuadXRegression4TSTest3 extends App

    The QuadXRegression4TSTest3 object is used to test the QuadXRegression4TS class.

    The QuadXRegression4TSTest3 object is used to test the QuadXRegression4TS class. The 'x' matrix in two dimensional. > runMain scalation.analytics.forecaster.QuadXRegression4TSTest3

  127. object QuadXRegression4TSTest4 extends App

    The QuadXRegression4TSTest4 object tests the QuadXRegression4TS class using the AutoMPG dataset.

    The QuadXRegression4TSTest4 object tests the QuadXRegression4TS class using the AutoMPG dataset. It illustrates using the Relation class for reading the data from a .csv file "auto-mpg.csv". Assumes no missing values. It also combines feature selection with cross-validation and plots R2, R2 Bar and R^2 cv vs. the instance index. > runMain scalation.analytics.forecaster.QuadXRegression4TSTest4

  128. object QuadXRegression4TSTest5 extends App

    The QuadXRegression4TSTest5 object tests QuadXRegression4TS class using the following regression equation.

    The QuadXRegression4TSTest5 object tests QuadXRegression4TS class using the following regression equation.

    y = b dot x = b_0 + b_1*x1 + b_2*x_2.

    > runMain scalation.analytics.forecaster.QuadXRegression4TSTest5

  129. object QuadXRegression4TSy extends ModelFactory

    The QuadXRegression4TSy companion object provides factory functions and functions for creating functional forms.

  130. object QuadXRegression4TSyTest extends App

    The QuadXRegression4TSyTest object is used to test the QuadXRegression4TSy class.

    The QuadXRegression4TSyTest object is used to test the QuadXRegression4TSy class. > runMain scalation.analytics.forecaster.QuadXRegression4TSyTest

  131. object QuadXRegression4TSyTest2 extends App

    The QuadXRegression4TSyTest2 object is used to test the QuadXRegression4TSy class.

    The QuadXRegression4TSyTest2 object is used to test the QuadXRegression4TSy class. The order 'p' hyper-parameter should be re-assigned (try 1, 2, 3, ...). > runMain scalation.analytics.forecaster.QuadXRegression4TSyTest2

  132. object QuadXRegression4TSyTest3 extends App

    The QuadXRegression4TSyTest3 object is used to test the QuadXRegression4TSy class.

    The QuadXRegression4TSyTest3 object is used to test the QuadXRegression4TSy class. Forecasting lake levels.

    See also

    cran.r-project.org/web/packages/fpp/fpp.pdf > runMain scalation.analytics.forecaster.QuadXRegression4TSyTest3

  133. object RandomWalk

    The RandomWalk companion object provides factory methods for the RandomWalk class.

  134. object RandomWalkTest extends App

    The RandomWalkTest object is used to test the RandomWalk class.

    The RandomWalkTest object is used to test the RandomWalk class. > runMain scalation.analytics.forecaster.RandomWalkTest

  135. object RandomWalkTest2 extends App

    The RandomWalkTest2 object is used to test the RandomWalk class.

    The RandomWalkTest2 object is used to test the RandomWalk class. The order hyper-parameter 'p' is re-assigned. > runMain scalation.analytics.forecaster.RandomWalkTest2

  136. object RandomWalkTest3 extends App

    The RandomWalkTest3 object is used to test the RandomWalk class.

    The RandomWalkTest3 object is used to test the RandomWalk class. Forecasting lake levels.

    See also

    cran.r-project.org/web/packages/fpp/fpp.pdf > runMain scalation.analytics.forecaster.RandomWalkTest3

  137. object RandomWalkTest4 extends App

    The RandomWalkTest4 object is used to test the RandomWalk class.

    The RandomWalkTest4 object is used to test the RandomWalk class. Forecasting travel times. > runMain scalation.analytics.forecaster.RandomWalkTest4

  138. object RandomWalkTest5 extends App

    The RandomWalkTest5 object is used to test the RandomWalk class.

    The RandomWalkTest5 object is used to test the RandomWalk class. Having a quadratic trend. > runMain scalation.analytics.forecaster.RandomWalkTest5

  139. object RandomWalkTest6 extends App

    The RandomWalkTest6 object is used to test the RandomWalk class.

    The RandomWalkTest6 object is used to test the RandomWalk class. Using a Random Walk model on white noise. > runMain scalation.analytics.forecaster.RandomWalkTest6

  140. object Regression4TS extends ModelFactory

    The Regression4TS companion object provides factory functions and functions for creating functional forms.

  141. object Regression4TSTest extends App

    The Regression4TSTest object is used to test the Regression4TS class.

    The Regression4TSTest object is used to test the Regression4TS class. > runMain scalation.analytics.forecaster.Regression4TSTest

  142. object Regression4TSTest2 extends App

    The Regression4TSTest2 object is used to test the Regression4TS class.

    The Regression4TSTest2 object is used to test the Regression4TS class. The 'x' matrix in one dimensional. > runMain scalation.analytics.forecaster.Regression4TSTest2

  143. object Regression4TSTest3 extends App

    The Regression4TSTest3 object is used to test the Regression4TS class.

    The Regression4TSTest3 object is used to test the Regression4TS class. The 'x' matrix in two dimensional. > runMain scalation.analytics.forecaster.Regression4TSTest3

  144. object Regression4TSTest4 extends App

    The Regression4TSTest4 object tests the Regression4TS class using the AutoMPG dataset.

    The Regression4TSTest4 object tests the Regression4TS class using the AutoMPG dataset. It illustrates using the Relation class for reading the data from a .csv file "auto-mpg.csv". Assumes no missing values. It also combines feature selection with cross-validation and plots R2, R2 Bar and R^2 cv vs. the instance index. > runMain scalation.analytics.forecaster.Regression4TSTest4

  145. object Regression4TSTest5 extends App

    The Regression4TSTest5 object tests Regression4TS class using the following regression equation.

    The Regression4TSTest5 object tests Regression4TS class using the following regression equation.

    y = b dot x = b_0 + b_1*x1 + b_2*x_2.

    > runMain scalation.analytics.forecaster.Regression4TSTest5

  146. object Regression4TSy extends ModelFactory

    The Regression4TSy companion object provides factory functions and functions for creating functional forms.

  147. object Regression4TSyTest extends App

    The Regression4TSyTest object is used to test the Regression4TSy class.

    The Regression4TSyTest object is used to test the Regression4TSy class. > runMain scalation.analytics.forecaster.Regression4TSyTest

  148. object Regression4TSyTest2 extends App

    The Regression4TSyTest2 object is used to test the Regression4TSy class.

    The Regression4TSyTest2 object is used to test the Regression4TSy class. The order 'p' hyper-parameter should be re-assigned (try 1, 2, 3, ...). > runMain scalation.analytics.forecaster.Regression4TSyTest2

  149. object Regression4TSyTest3 extends App

    The Regression4TSyTest3 object is used to test the Regression4TSy class.

    The Regression4TSyTest3 object is used to test the Regression4TSy class. Forecasting lake levels.

    See also

    cran.r-project.org/web/packages/fpp/fpp.pdf > runMain scalation.analytics.forecaster.Regression4TSyTest3

  150. object RollingValidation

    The RollingValidation object provides 'k'-fold rolling validations, e.g., for 'm = 1200' and 'k = 10', 'kt = 20':

    The RollingValidation object provides 'k'-fold rolling validations, e.g., for 'm = 1200' and 'k = 10', 'kt = 20':

    1: tr(ain) 0 until 800, te(st) 800 until 840 2: tr(ain) 40 until 840, te(st) 840 until 880 3: tr(ain) 80 until 880, te(st) 880 until 920 4: tr(ain) 120 until 920, te(st) 920 until 960 5: tr(ain) 160 until 960, te(st) 960 until 1000 6: tr(ain) 200 until 1000, te(st) 1000 until 1040 7: tr(ain) 240 until 1040, te(st) 1040 until 1080 8: tr(ain) 280 until 1080, te(st) 1080 until 1120 9: tr(ain) 320 until 1120, te(st) 1120 until 1160 10: tr(ain) 360 until 1160, te(st) 1160 until 1200

    In rolling validation for this case, each training dataset has 800 instances, while each testing dataset has 40. Re-training occurs before every 'kt = 20' forecasts are made (2 re-trainings per testing dataset for this case).

  151. object RollingValidationTest extends App

    The RollingValidationTest object is used to test the 'crossValidate' method in the RollingValidation object.

    The RollingValidationTest object is used to test the 'crossValidate' method in the RollingValidation object. > runMain scalation.analytics.forecaster.RollingValidationTest

  152. object RollingValidationTest2 extends App

    The RollingValidationTest2 object is used to test the 'crossValidate2' method in the RollingValidation object.

    The RollingValidationTest2 object is used to test the 'crossValidate2' method in the RollingValidation object. > runMain scalation.analytics.forecaster.RollingValidationTest2

  153. object SARIMA

    Companion object for class SARIMA.

    Companion object for class SARIMA. Includes features related to differencing and automated order selection.

    See also

    www.jstatsoft.org/article/view/v027i03/v27i03.pdf

  154. object SARIMATest extends App

    The SARIMATest object is used to test the SARIMA class.

    The SARIMATest object is used to test the SARIMA class. > runMain scalation.analytics.forecaster.SARIMATest

  155. object SARIMATest2 extends App

    The SARIMATest2 object is used to test the SARIMA class.

    The SARIMATest2 object is used to test the SARIMA class. > runMain scalation.analytics.forecaster.SARIMATest2

  156. object SARIMATest3 extends App

    The SARIMATest3 object is used to test the SARIMA class.

    The SARIMATest3 object is used to test the SARIMA class. Forecasting lake levels.

    See also

    cran.r-project.org/web/packages/fpp/fpp.pdf > runMain scalation.analytics.forecaster.SARIMATest3

  157. object SARIMATest4 extends App

    The SARIMATest4 object is used to test the SARIMA class.

    The SARIMATest4 object is used to test the SARIMA class. > runMain scalation.analytics.forecaster.SARIMATest4

  158. object SARIMATest5 extends App

    The SARIMATest5 object is used to test the SARIMA class.

    The SARIMATest5 object is used to test the SARIMA class. > runMain scalation.analytics.forecaster.SARIMATest5

  159. object SARIMATest6 extends App

    The SARIMATest6 object is used to test the SARIMA class on real data: Forecasting lake levels.

    The SARIMATest6 object is used to test the SARIMA class on real data: Forecasting lake levels. Select the best number of lags.

    See also

    cran.r-project.org/web/packages/fpp/fpp.pdf > runMain scalation.analytics.forecaster.SARIMATest6

  160. object SARIMAX

    Companion object for class SARIMAX.

    Companion object for class SARIMAX. Includes features related to differencing and automated order selection.

    See also

    www.jstatsoft.org/article/view/v027i03/v27i03.pdf

  161. object SARIMAXTest extends App

    The SARIMAXTest object is used to test the SARIMAX class.

    The SARIMAXTest object is used to test the SARIMAX class. > runMain scalation.analytics.forecaster.SARIMAXTest

  162. object SARIMAXTest2 extends App

    The SARIMAXTest2 object is used to test the SARIMAX class.

    The SARIMAXTest2 object is used to test the SARIMAX class. > runMain scalation.analytics.forecaster.SARIMAXTest2

  163. object SARIMAXTest3 extends App

    The SARIMAXTest3 object is used to test the SARIMAX class.

    The SARIMAXTest3 object is used to test the SARIMAX class. Forecasting lake levels.

    See also

    cran.r-project.org/web/packages/fpp/fpp.pdf > runMain scalation.analytics.forecaster.SARIMAXTest3

  164. object SARIMAXTest4 extends App

    The SARIMAXTest4 object is used to test the SARIMAX class.

    The SARIMAXTest4 object is used to test the SARIMAX class. > runMain scalation.analytics.forecaster.SARIMAXTest4

  165. object SimpleExpSmoothing

    The SimpleExpSmoothing companion object provides factory methods for the SimpleExpSmoothing class.

  166. object SimpleExpSmoothingTest extends App

    The SimpleExpSmoothingTest object is used to test the SimpleExpSmoothing class.

    The SimpleExpSmoothingTest object is used to test the SimpleExpSmoothing class. Test customized smoothing (call 'smooth') versus optimized smoothing (call 'train'). > runMain scalation.analytics.forecaster.SimpleExpSmoothingTest

  167. object SimpleExpSmoothingTest2 extends App

    The SimpleExpSmoothingTest2 object is used to test the SimpleExpSmoothing class.

    The SimpleExpSmoothingTest2 object is used to test the SimpleExpSmoothing class. Test rolling validation. > runMain scalation.analytics.forecaster.SimpleExpSmoothingTest2

  168. object SimpleExpSmoothingTest3 extends App

    The SimpleExpSmoothingTest3 object is used to test the SimpleExpSmoothing class.

    The SimpleExpSmoothingTest3 object is used to test the SimpleExpSmoothing class. Forecasting lake levels.

    See also

    cran.r-project.org/web/packages/fpp/fpp.pdf > runMain scalation.analytics.forecaster.SimpleExpSmoothingTest3

  169. object SimpleExpSmoothingTest4 extends App

    The SimpleExpSmoothingTest4 object is used to test the SimpleExpSmoothing class.

    The SimpleExpSmoothingTest4 object is used to test the SimpleExpSmoothing class. Forecasting lake levels for several values of the smoothing parameter α.

    See also

    cran.r-project.org/web/packages/fpp/fpp.pdf > runMain scalation.analytics.forecaster.SimpleExpSmoothingTest4

  170. object SimpleRollingValidation

    The SimpleRollingValidation object provides '1'-fold rolling validations, e.g., for 'm = 1200' and 'k = 1', 'kt = 5':

    The SimpleRollingValidation object provides '1'-fold rolling validations, e.g., for 'm = 1200' and 'k = 1', 'kt = 5':

    1: tr(ain) 0 until 600, te(st) 600 until 1200

    In rolling validation for this case, each retraining dataset has 600 instances, while the testing dataset has 600. Re-training occurs before every 'kt = 2' forecasts are made.

  171. object SimpleRollingValidationTest extends App

    The SimpleRollingValidationTest object is used to test the 'crossValidate' method in the SimpleRollingValidation object.

    The SimpleRollingValidationTest object is used to test the 'crossValidate' method in the SimpleRollingValidation object. > runMain scalation.analytics.forecaster.SimpleRollingValidationTest

  172. object SimpleRollingValidationTest2 extends App

    The SimpleRollingValidationTest2 object is used to test the 'crossValidate2' method in the SimpleRollingValidation object.

    The SimpleRollingValidationTest2 object is used to test the 'crossValidate2' method in the SimpleRollingValidation object. > runMain scalation.analytics.forecaster.SimpleRollingValidationTest2

Inherited from Error

Inherited from AnyRef

Inherited from Any

Ungrouped