$ wf g -w /path/to/warp10 io.warp10 warp10-ext-forecasting 1.0.3

/path/to/warp10/ is where Warp 10 is physically located.



This repository only contains the documentation of warp10-ext-forecasting.

The documentation is located under src/main/warpscript/io.warp10/warp10-ext-forecasting/.

These functions are available on the Warp 10 instance of the sandbox.

To install this extension on-premise or to make it available on your saas plan, please contact us.

Forecast extension for the WarpScript language

Functions that build GTS forecast models:

RANDOMWALK           // build a random walk model
SRANDOMWALK          // build a random walk model that is seeded with PRNG function
LSTM                 // build an LSTM neural network model
NNETAR               // build a neural network auto-regressive model
SES                  // build a simple exponential smoothing model
HOLT                 // build a Holt's linear model (SES + trend)
HOLTWINTERS          // build a Holt-Winters' model (SES + trend + seasonal)
ARMA                 // build an auto-regressive moving average model
ARIMA                // build an auto-regressive integrated moving average model
SARMA                // build a seasonal auto-regressive moving average model
SARIMA               // build a seasonal auto-regressive integrated moving average model
SEARCH.ETS           // search for a suitable exponential trend-seasonal model (include SES, HOLT and HOLTWINTERS)
SEARCH.ARIMA         // search for a suitable Arima model (include ARMA and ARIMA)
SEARCH.SARIMA        // search for a suitable Sarima model (include SARMA and SARIMA)
SEARCH.NNET          // search for a suitable neural network model (include LSTM and NNETAR)
AUTO                 // automatically choose a forecast model (include all of the above but ignore seasonal component)

Functions that take a GTS forecast model as argument:

FORECAST                 // forecast values in the future
FORECAST.ADDVALUES       // forecast values in the future and append them to observation GTS
INFORECAST               // produce in-sample one-step ahead forecasts
CROSSFORECAST            // forecast values given a model fitted with another GTS
CROSSFORECAST.ADDVALUES  // forecast values given a model fitted with another GTS and append them to input GTS
FORECAST.ANOMALIES       // detect anomalies in in-sample forecast
FORECAST.ANOMALIES.DROP  // detect anomalies in in-sample forecast and drop them from input GTS
MODELINFO // return map of information about the model AIC // compute Akaike information criterion

Functions related to stationarity and differencing:

STATIONARY     // test whether input GTS is stationary
DIFF           // apply time differencing with one or more seasonalities
INVERTDIFF     // integrate with one or more seasonalities

Fit / Transform / Inverse-Transform programming pattern (similar to sklearn)

FIT               // fit a GTS transformer
TRANSFORM         // transform a GTS using a GTS transformer
INVERSETRANSFORM  // inverse-transform a GTS using a GTS transformer
GTSTRANSFORMER    // build a GTS transformer from a set of macros
DIFFERENCER       // build a GTS transformer for time differencing


<GTS> AUTO 5 FORECAST pushes onto the stack a GTS with 5 forecast ticks.

<GTS> AUTO 5 FORECAST.ADDVALUES merges a GTS with its forecast.


Jean-Charles Vialatte





Last published



SenX private



Path Size Creation time
  AIC 906 bytes 2020-01-16
  ARIMA 1545 bytes 2020-01-16
  ARMA 1474 bytes 2020-01-16
  AUTO 898 bytes 2020-01-16
  CROSSFORECAST 1397 bytes 2020-01-16
  CROSSFORECAST.ADDVALUES 1172 bytes 2020-01-16
  DIFF 722 bytes 2020-01-16
  DIFFERENCER 1177 bytes 2020-01-16
  FIT 773 bytes 2020-01-16
  FORECAST 1289 bytes 2020-01-16
  FORECAST.ADDVALUES 1075 bytes 2020-01-16
  FORECAST.ANOMALIES 1153 bytes 2020-01-16
  FORECAST.ANOMALIES.DROP 1158 bytes 2020-01-16
  GTSTRANSFORMER 1107 bytes 2020-01-16
  HOLT 1793 bytes 2020-01-16
  HOLTWINTERS 2132 bytes 2020-01-16
  INFORECAST 961 bytes 2020-01-16
  INVERSETRANSFORM 738 bytes 2020-01-16
  INVERTDIFF 974 bytes 2020-01-16
  LSTM 1631 bytes 2020-01-16
  MODELINFO 925 bytes 2020-01-16
  NNETAR 1965 bytes 2020-01-16
  RANDOMWALK 1094 bytes 2020-01-16
  SARIMA 1895 bytes 2020-01-16
  SARMA 1746 bytes 2020-01-16
  SEARCH.ARIMA 1361 bytes 2020-01-16
  SEARCH.ETS 1210 bytes 2020-01-16
  SEARCH.NNET 1412 bytes 2020-01-16
  SEARCH.SARIMA 1614 bytes 2020-01-16
  SES 1459 bytes 2020-01-16
  SRANDOMWALK 1273 bytes 2020-01-16
  STATIONARY 940 bytes 2020-01-16
  TRANSFORM 725 bytes 2020-01-16