| name | choosing-a-forecaster |
| description | Guides selection of the appropriate skforecast forecaster based on the user's data characteristics and requirements. Provides a decision matrix mapping use cases to forecaster classes. Use when the user is unsure which forecaster to use or asks for a recommendation.
|
Choosing a Forecaster
When to Use This Skill
Use this skill when the user needs help choosing a forecaster, comparing forecaster types (recursive vs direct, single vs multi-series), or understanding which skforecast class fits their problem.
Related skills
- After:
forecasting-single-series (apply the chosen forecaster to one target series)
- After:
forecasting-multiple-series (apply the chosen forecaster to several series jointly)
- After:
autocorrelation-and-lag-selection (analyse the series dynamics before configuring lags)
- After:
feature-engineering (build the input feature set: calendar, rolling, exogenous)
Overview
Skforecast is a machine learning-first library. The ML forecasters are the primary tools; statistical models (ForecasterStats) and naive baselines (ForecasterEquivalentDate) serve as comparison benchmarks.
Step 1 — How Many Series?
| Scenario | Go to |
|---|
| 1 target series (with or without exogenous variables) | → Step 2a |
| Multiple series to forecast simultaneously | → Step 2b |
| Multiple series as drivers to predict one target | → ForecasterDirectMultiVariate |
| Categorical target (e.g., low/medium/high) | → ForecasterRecursiveClassifier |
Step 2a — Single Series
| Scenario | Recommended Forecaster | Why |
|---|
| General purpose (start here) | ForecasterRecursive | Default choice. One model, recursive multi-step. Works with any sklearn-compatible estimator (LightGBM, XGBoost, CatBoost, RandomForest, etc.). Supports lags, window features, exog, differentiation, transformers, weight functions, and all probabilistic prediction methods (bootstrapping, conformal, quantiles, distributions) |
| Horizon-dependent patterns (e.g., predicting at 1h vs 24h requires different relationships) | ForecasterDirect | Trains one independent model per step — no error propagation. Better when the predictive relationship changes significantly across the forecast horizon. Requires steps at init; parallelizable with n_jobs |
| Statistical baseline | ForecasterStats | Wraps ARIMA, SARIMAX, ETS, ARAR. Use as a benchmark to compare against ML models, or when the series is very short (< 200 obs) and ML overfits |
| Zero-shot / cold-start / no training data | ForecasterFoundation | Wraps pre-trained foundation models (Chronos-2, TimesFM 2.5, Moirai-2, TabICL, TabPFN-TS, TFC-T0). fit() only stores context — no training. Good baseline and cold-start option. See the foundation-forecasting skill |
| Naive baseline | ForecasterEquivalentDate | Predicts using equivalent past dates (e.g., same weekday last week). Use as a sanity-check baseline |
Step 2b — Multiple Series
| Scenario | Recommended Forecaster | Why |
|---|
| Forecast many series with a shared model (start here) | ForecasterRecursiveMultiSeries | One global model learns cross-series patterns. Supports DataFrame or dict input (dict allows series with different date ranges). Encoding options: 'ordinal' (default), 'ordinal_category', 'onehot', None. Supports per-series transformers, per-series differentiation, series_weights |
| Other series are features for one target | ForecasterDirectMultiVariate | All series become input features to predict a single level. Per-series lags via dict ({'sales': [1,7], 'price': [1]}). One model per step — no error propagation |
| Deep learning / complex nonlinear patterns | ForecasterRnn | Keras-based RNN/LSTM/GRU. Single model outputs all steps and levels simultaneously via 3D tensors. Only conformal intervals (no bootstrapping). Requires keras |
| Zero-shot / pre-trained generalist | ForecasterFoundation | Global zero-shot forecasts via Chronos-2 / TimesFM 2.5 / Moirai-2 / TabICL / TabPFN-TS / TFC-T0. fit() only stores context. Native quantile intervals. Chronos-2, TabICL, TabPFN-TS, and TFC-T0 support exog; TimesFM 2.5 & Moirai-2 do not. See the foundation-forecasting skill |
Decision Flowchart
How many series?
│
├─► 1 series
│ │
│ ├─► Is it a classification problem? ──► Yes ──► ForecasterRecursiveClassifier
│ │
│ └─► Regression (continuous target)
│ │
│ ├─► Does the forecast relationship change across the horizon?
│ │ ├─► No / Unsure ──► ForecasterRecursive ← START HERE
│ │ └─► Yes (step-specific patterns) ──► ForecasterDirect
│ │
│ └─► Compare with baselines:
│ ├─► ForecasterStats (Auto-ARIMA, ETS) as statistical benchmark
│ └─► ForecasterEquivalentDate as naive benchmark
│
└─► Multiple series
│
├─► Want to forecast ALL series (global model)?
│ └─► ForecasterRecursiveMultiSeries ← START HERE
│
├─► Want to use other series AS FEATURES for one target?
│ └─► ForecasterDirectMultiVariate
│
└─► Need deep learning for very complex patterns?
└─► ForecasterRnn
Zero-shot / no training data / cold-start (single or multi-series)?
└─► ForecasterFoundation (Chronos-2 / TimesFM 2.5 / Moirai-2 / TabICL / TabPFN-TS / TFC-T0)
Key Comparisons
Recursive vs Direct (Single Series)
| Aspect | ForecasterRecursive | ForecasterDirect |
|---|
| Models trained | 1 | N (one per step) |
| Error propagation | Yes (predictions feed into next step) | No (each step uses only observed data) |
| Forecast horizon | Flexible (any steps at predict time) | Fixed at init (steps required) |
| Training speed | Fast (one model) | Slower (N models, parallelizable via n_jobs) |
| Memory | Lower (1 estimator) | Higher (N estimators stored in estimators_ dict) |
| Best for | Most cases, especially short-to-medium horizons | Long horizons where patterns change per step |
| Prediction intervals | Bootstrapping + conformal | Bootstrapping + conformal |
RecursiveMultiSeries vs DirectMultiVariate (Multiple Series)
| Aspect | ForecasterRecursiveMultiSeries | ForecasterDirectMultiVariate |
|---|
| Goal | Forecast all series with one model | Use all series as features for one target |
| Input | DataFrame or dict of Series | DataFrame (all series as columns) |
| Target | All series (selected via levels) | One series (specified via level) |
| Strategy | Recursive (1 model, predictions feed back) | Direct (1 model per step, no error propagation) |
| Series identification | Encoding: 'ordinal', 'ordinal_category', 'onehot', None | All series create separate lag columns |
| Series with different ranges | Yes (via dict input) | No (all must share same range) |
| Per-series lags | No (same lags for all) | Yes (dict: {'sales': [1,7], 'price': [1]}) |
| Series weights | Yes (series_weights param) | No |
| Per-series transformers | Yes (transformer_series dict) | Yes (transformer_series dict) |
| Per-series differentiation | Yes (differentiation dict) | Yes (via differentiator_ per series) |
ML Forecasters vs Statistical Models
| Aspect | ML Forecasters | ForecasterStats |
|---|
| Primary role | Main forecasting tools | Comparison baseline |
| Best for | Medium-to-large datasets, complex patterns, many exogenous features | Short series, interpretability, parametric intervals |
| Estimators | Any sklearn-compatible (LightGBM, XGBoost, RF, etc.) | Arima, Sarimax, Ets, Arar |
| Lags / window features | Full support (lags, RollingFeatures) | No (model handles its own structure) |
| Differentiation | Built-in (differentiation param) | Handled within model (e.g., ARIMA d parameter) |
| Exogenous variables | Full support | Only SARIMAX |
| Prediction intervals | Bootstrapping (binned residuals) + conformal | Built-in parametric |
| Tuning | grid_search_forecaster, random_search_forecaster, bayesian_search_forecaster | grid_search_stats, random_search_stats |
| Backtesting | backtesting_forecaster / backtesting_forecaster_multiseries | backtesting_stats |
| Feature selection | select_features / select_features_multiseries | Not applicable |
Feature Support Matrix
| Feature | Recursive | Direct | RecursiveMultiSeries | DirectMultiVariate | Rnn | Stats | EquivalentDate | Classifier |
|---|
| Lags | ✓ | ✓ | ✓ | ✓ (per-series dict) | ✓ | — | — | ✓ |
Window features (RollingFeatures) | ✓ | ✓ | ✓ | ✓ | — | — | — | ✓ |
| Exogenous variables | ✓ | ✓ | ✓ | ✓ | ✓ | SARIMAX only | — | ✓ |
| Differentiation | ✓ | ✓ | ✓ (per-series) | ✓ (per-series) | — | Within model | — | — |
| Transformer y/series | ✓ | ✓ | ✓ (per-series) | ✓ (per-series) | ✓ | ✓ | — | — |
| Transformer exog | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | — | ✓ |
| Weight function | ✓ | ✓ | ✓ (per-series) | ✓ | — | — |
Legend: ✓ = supported, — = not supported/not applicable.
Quick Start Recommendations
Once you have chosen a forecaster, follow these steps to get started:
- Define your problem: 1 series →
ForecasterRecursive; multiple series → ForecasterRecursiveMultiSeries
- Choose an estimator: LightGBM (
LGBMRegressor) is the best starting point — fast, handles categoricals, good defaults
- Add features: Use
RollingFeatures (rolling mean, std, min, max) and CalendarFeatures or create_calendar_features as exogenous variables
- Handle non-stationarity: Use the
differentiation parameter instead of manual differencing
- Evaluate with backtesting:
backtesting_forecaster + TimeSeriesFold for realistic multi-step evaluation
- Tune hyperparameters:
bayesian_search_forecaster (Optuna-based) — can include lags in the search space
- Add prediction intervals:
predict_interval(method='bootstrapping', use_binned_residuals=True) for uncertainty quantification
- Compare with baselines: Use
ForecasterStats (Auto-ARIMA: Arima(order=None)) and ForecasterEquivalentDate to verify the ML model adds value
