$ wf g -w /path/to/warp10 io.warp10 warp10-ext-forecasting 1.0.0-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
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.