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Fincept TS Forecast is the statistical forecasting engine behind every tsforecast_* tool: automatic and fixed-order ARIMA, ETS, complex exponential smoothing and Theta; Holt and Holt-Winters; TBATS, MSTL and boosted decomposition for multiple seasonality; unobserved components; Croston, ADIDA, IMAPA and TSB for intermittent demand; GARCH and ARCH volatility; naive, seasonal naive, drift and window-average benchmarks; and regression on exogenous columns, fitted one model per series on one series or a whole panel. Native or conformal prediction intervals, rolling-origin backtests with accuracy metrics, sample-path simulation, fitted-model summaries and transfer of fitted models to new data complete it. It runs on Fincept’s servers; your agent sends numbers or data references and gets back a result envelope with tables and charts.
Fincept TS Forecast tools are free. They count toward the rate limit only; a $fincept reference inside a call is charged like a direct call to that tool.

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Modules

Tools are grouped in modules. Each module is a toolset named tsforecast_<module>, so you can list it or search within it. The Fincept TS Forecast reference lists every module and tool. tsforecast_catalog returns the live list of modules and tools from inside your agent.

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Inputs

The id, time and target columns are unique_id, ds and y unless id_col, time_col and target_col name others. h sets the horizon and levels the interval levels in percent ([80, 95] by default; [] for point forecasts). tsforecast_garch takes returns, not prices.

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Limits

A computation that runs past its time limit answers timeout; a busy engine answers busy and the call can be retried a few seconds later.

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Outputs

Forecasting tools return the forecast of every series and model with -lo-<level> and -hi-<level> interval bands and charts; the family tools add each fitted model’s specification, coefficients and information criteria, and the decomposition tools their trend, seasonal and remainder components. tsforecast_cross_validation returns every window’s forecasts, the chosen metrics per model, window and series and the best model; tsforecast_evaluate scores any forecasts against actuals. tsforecast_simulate returns the mean, standard deviation and quantiles of the simulated paths as a fan chart, with a few raw paths.

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Example

Prompt
The agent searches for statistical forecast, finds tsforecast_cross_validation and tsforecast_forecast, and passes the closes as y and their dates as dates, both $fincept references to market_get_candles (candles.close and candles.time).