StatsForecast offers a collection of popular univariate time series forecasting models optimized for high performance and scalability.
StatsForecast
with:
StatsForecast
includes an extensive battery of models that can efficiently fit
millions of time series.
AutoARIMA
,
AutoETS
,
AutoCES
,
MSTL
and
Theta
in Python..fit
and .predict
.exogenous variables
and prediction intervals
for
ARIMA.pmdarima
.R
.Prophet
.statsmodels
.numba
.Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | Exogenous features |
---|---|---|---|---|---|
AutoARIMA | ✅ | ✅ | ✅ | ✅ | ✅ |
AutoETS | ✅ | ✅ | ✅ | ✅ | |
AutoCES | ✅ | ✅ | ✅ | ✅ | |
AutoTheta | ✅ | ✅ | ✅ | ✅ | |
AutoMFLES | ✅ | ✅ | ✅ | ✅ | ✅ |
AutoTBATS | ✅ | ✅ | ✅ | ✅ |
Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | Exogenous features |
---|---|---|---|---|---|
ARIMA | ✅ | ✅ | ✅ | ✅ | ✅ |
AutoRegressive | ✅ | ✅ | ✅ | ✅ | ✅ |
Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | Exogenous features |
---|---|---|---|---|---|
Theta | ✅ | ✅ | ✅ | ✅ | |
OptimizedTheta | ✅ | ✅ | ✅ | ✅ | |
DynamicTheta | ✅ | ✅ | ✅ | ✅ | |
DynamicOptimizedTheta | ✅ | ✅ | ✅ | ✅ |
Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | Exogenous features |
---|---|---|---|---|---|
MSTL | ✅ | ✅ | ✅ | ✅ | If trend forecaster supports |
MFLES | ✅ | ✅ | ✅ | ✅ | ✅ |
TBATS | ✅ | ✅ | ✅ | ✅ |
Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | Exogenous features |
---|---|---|---|---|---|
GARCH | ✅ | ✅ | ✅ | ✅ | |
ARCH | ✅ | ✅ | ✅ | ✅ |
Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | Exogenous features |
---|---|---|---|---|---|
HistoricAverage | ✅ | ✅ | ✅ | ✅ | |
Naive | ✅ | ✅ | ✅ | ✅ | |
RandomWalkWithDrift | ✅ | ✅ | ✅ | ✅ | |
SeasonalNaive | ✅ | ✅ | ✅ | ✅ | |
WindowAverage | ✅ | ||||
SeasonalWindowAverage | ✅ |
SimpleExponential
family for data with no
clear trend or seasonality.
Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | Exogenous features |
---|---|---|---|---|---|
SimpleExponentialSmoothing | ✅ | ||||
SimpleExponentialSmoothingOptimized | ✅ | ||||
SeasonalExponentialSmoothing | ✅ | ||||
SeasonalExponentialSmoothingOptimized | ✅ | ||||
Holt | ✅ | ✅ | ✅ | ✅ | |
HoltWinters | ✅ | ✅ | ✅ | ✅ |
Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | Exogenous features |
---|---|---|---|---|---|
ADIDA | ✅ | ✅ | ✅ | ||
CrostonClassic | ✅ | ✅ | ✅ | ||
CrostonOptimized | ✅ | ✅ | ✅ | ||
CrostonSBA | ✅ | ✅ | ✅ | ||
IMAPA | ✅ | ✅ | ✅ | ||
TSB | ✅ | ✅ | ✅ |
Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | Exogenous features |
---|---|---|---|---|---|
SklearnModel | ✅ | ✅ | ✅ | ✅ |