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

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

2.  class ARIMA extends Forecaster

   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. ARIMA 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) + 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. ------------------------------------------------------------------------------

3.  class ExpSmoothing extends Forecaster

   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.

4.  trait Forecaster extends Error

   The Forecaster trait provides a common framework for several forecasters.

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

5.  abstract class ForecasterVec extends Forecaster

   The Forecaster trait provides a common framework for several forecasters.

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

6.  class KPSS 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.

7.  class KalmanFilter extends AnyRef

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

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

   See also

   en.wikipedia.org/wiki/Kalman_filter FIX: needs more thorough testing

8.  class SARIMA extends Forecaster

   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 an '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. SARIMA 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) + 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. Exogeous/External regressor may also be added to the right-hand size of the model in a similar manner to Regression models. ------------------------------------------------------------------------------

9.  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.