io.warp10:warp10-ext-forecasting:1.0.3
Installation
$ wf g -w /path/to/warp10 io.warp10 warp10-ext-forecasting 1.0.3
/path/to/warp10/
is where Warp 10 is physically located.
Readme
Notice
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
Examples
<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.
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 |