$ wf g -w /path/to/warp10 io.warp10 warp10-ext-forecasting 1.0.1-uberjar

/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 your own instance, you can 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 890 bytes 2019-11-13
  ARIMA 1305 bytes 2019-11-13
  ARMA 1234 bytes 2019-11-13
  AUTO 885 bytes 2019-11-13
  CROSSFORECAST 1381 bytes 2019-11-13
  CROSSFORECAST.ADDVALUES 1156 bytes 2019-11-13
  DIFF 706 bytes 2019-11-13
  DIFFERENCER 1161 bytes 2019-11-13
  FIT 757 bytes 2019-11-13
  FORECAST 1273 bytes 2019-11-13
  FORECAST.ADDVALUES 1059 bytes 2019-11-13
  FORECAST.ANOMALIES 1137 bytes 2019-11-13
  FORECAST.ANOMALIES.DROP 1142 bytes 2019-11-13
  GTSTRANSFORMER 1091 bytes 2019-11-13
  HOLT 1553 bytes 2019-11-13
  HOLTWINTERS 1892 bytes 2019-11-13
  INFORECAST 945 bytes 2019-11-13
  INVERSETRANSFORM 722 bytes 2019-11-13
  INVERTDIFF 958 bytes 2019-11-13
  LSTM 1393 bytes 2019-11-13
  MODELINFO 909 bytes 2019-11-13
  NNETAR 1826 bytes 2019-11-13
  RANDOMWALK 1193 bytes 2019-11-13
  SARIMA 1655 bytes 2019-11-13
  SARMA 1506 bytes 2019-11-13
  SEARCH.ARIMA 1121 bytes 2019-11-13
  SEARCH.ETS 1197 bytes 2019-11-13
  SEARCH.NNET 1174 bytes 2019-11-13
  SEARCH.SARIMA 1375 bytes 2019-11-13
  SES 1219 bytes 2019-11-13
  SRANDOMWALK 1266 bytes 2019-11-13
  STATIONARY 924 bytes 2019-11-13
  TRANSFORM 709 bytes 2019-11-13