Packages

  • package root
    Definition Classes
    root
  • package scalation
    Definition Classes
    root
  • package analytics

    The analytics package contains classes, traits and objects for analytics including clustering and prediction.

    The analytics package contains classes, traits and objects for analytics including clustering and prediction.

    Definition Classes
    scalation
  • package classifier

    The analytics package contains classes, traits and objects for analytics focused on classification.

    The analytics package contains classes, traits and objects for analytics focused on classification.

    Definition Classes
    analytics
  • package clusterer

    The clusterer package contains classes, traits and objects for clustering algorithms.

    The clusterer package contains classes, traits and objects for clustering algorithms.

    Definition Classes
    analytics
  • package fda

    The fda package contains classes, traits and objects for Functional Data Analysis (FDA).

    The fda package contains classes, traits and objects for Functional Data Analysis (FDA).

    Definition Classes
    analytics
  • package forecaster
    Definition Classes
    analytics
  • ARIMA
  • ARIMATest
  • ARIMATest2
  • ARIMATest3
  • ARIMATest4
  • ExpSmoothing
  • ExpSmoothingTest
  • Forecaster
  • KalmanFilter
  • KalmanFilterTest
  • package par

    The par package contains classes, traits and objects for parallel analytics including clustering and prediction.

    The par package contains classes, traits and objects for parallel analytics including clustering and prediction.

    Definition Classes
    analytics
  • package recommender

    The recommender package contains classes, traits and objects of recommendation systems.

    The recommender package contains classes, traits and objects of recommendation systems.

    Definition Classes
    analytics

package forecaster

Ordering
  1. Alphabetic
Visibility
  1. Public
  2. All

Type Members

  1. class ARIMA extends Error

    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. ------------------------------------------------------------------------------

  2. class ExpSmoothing extends Forecaster with Error

    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. ------------------------------------------------------------------------------

  3. trait Forecaster extends AnyRef

    The Forecaster trait provides a common framework for several predictors.

    The Forecaster trait provides a common framework for several predictors. A predictor is for potentially unbounded responses (real or integer). When the number of distinct responses is bounded by some relatively small integer 'k', a classifier is likdely more appropriate. Note, the 'train' method must be called first followed by 'eval'.

  4. 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

Value Members

  1. 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.ARIMATest

  2. 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.ARIMATest2

  3. object ARIMATest3 extends App

    The ARIMATest3 object is used to test the ARIMA class.

    The ARIMATest3 object is used to test the ARIMA class. Forecasting lake levels.

    See also

    ??? > runMain scalation.analytics.ARIMATest3

  4. 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.ARIMATest4

  5. 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.ExpSmoothingTest

  6. 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.KalmanFilterTest

Ungrouped