Features
| Group | Examples | Leak-safe rule |
|---|---|---|
| Lags | y(t-H), ..., y(t-H-season+1), y(t-364) | minimum lag >= horizon (direct), or recursive prediction |
| Rolling statistics | mean/std/min/max over 7, 14, 28 periods | window ends at t-H |
| Calendar | day of week, month, day of year (sin/cos), week, holidays, paydays, events | known in advance |
| Known-future covariates | planned price, promotions, opening hours | only values fixed at forecast time |
| Static | store size, product category, region | fine |
| Series id | categorical id in a global model | fine |
| Trend handling | target / rolling mean, differences, or linear detrend | fit within the training window |
Tree models cannot predict outside the target range seen in training, so an upward trend gets flattened.
Detrend (the generated script's --detrend fits a linear trend per training window), model ratios to a
rolling level, or difference the target. On the fixture, detrending cut MASE from 1.088 to 0.889.
Strategies for multi-step forecasts
| Strategy | How | Pros / cons |
|---|---|---|
| Recursive | one-step model, feed predictions back as lags | one model; errors compound; needs lag 1 features |
| Direct (one model per step) | H models, model h uses lags >= h | no compounding; H models |
| Direct with minimum lag = H (generated script) | one model, all lags >= H | one model, leak-free for every step; loses the most recent information for short steps |
| Multi-output (MIMO) | predict the H-vector at once | captures step dependence; needs suitable learners |
Global models
One model trained on many related series, with series id or static features, usually beats per-series models when series are short or noisy. This was the case in M5, where LightGBM global models won.
panel["series_id"] = panel["store"].astype("category")
# features computed per series with groupby(...).shift(H).rolling(...); then one LGBMRegressor on all rowsNormalise targets per series (divide by the series mean) so large series do not dominate, and add the scale back after prediction. Use the Tweedie objective for zero-inflated sales.
Hierarchies
Store x product x region totals must add up. Forecast at each level and reconcile with bottom-up, top-down
or MinT (hierarchicalforecast package). MinT usually improves accuracy at all levels.
Intervals
| Method | Notes |
|---|---|
| Split-conformal on backtest residuals (generated script) | distribution-free; calibrated only for regimes seen in the folds |
Quantile regression (LightGBM objective="quantile", alpha=0.1 / 0.9) | one model per quantile; can cross |
| Model-based (ETS/ARIMA) | assume the model is right; often too narrow |
| Bootstrapped residual paths | for recursive models |
Check coverage on a later holdout. On the fixture: 0.71-0.86 in normal months for 80% intervals, but 0.46 in the December peak that the folds did not include.
Production
- Retrain on a schedule (weekly) and backtest every retrain against the previous model and seasonal naive.
- Monitor error by horizon and bias, with labels arriving over the horizon. Alert on sustained bias.
- Log forecasts with their origin, so they can be evaluated exactly as in the backtest.
- Plan for structural breaks (new stores, product launches, pandemics): override or exclude periods, and add cold-start rules for new series (use the category average or similar series).
anomaly-detection-model-builder
Build and evaluate anomaly detection for tabular, sensor and transaction data with few or no labels: define the anomaly type (point, contextual, collective), choose detectors (robust z-score/MAD and IQR rules, Mahalanobis with robust covariance, k-NN distance, LOF, Isolation Forest, one-class SVM, PCA reconstruction / autoencoders, forecasting residuals for time series), combine them by rank, explain each alert by its deviating features, set thresholds from an alert budget or the score distribution, and evaluate with precision@k, PR-AUC and recall on known incidents, or detector agreement when no labels exist. Includes pure-Python detectors (robust z, Mahalanobis, kNN, Isolation Forest, ensemble) and an evaluation script.
catboost-model-builder
Build CatBoost models that use its strengths correctly: raw categorical columns as cat_features (ordered target statistics, no manual encoding), text_features, Pool construction with NaN-safe categories, ordered boosting and has_time for temporal data, symmetric (oblivious) trees, overfitting detector with eval_set and use_best_model, losses for classification, regression (Quantile, Poisson, Tweedie) and ranking (YetiRank), class weights, monotone constraints, feature importance (PredictionValuesChange, LossFunctionChange) and SHAP values, GPU training and model export. Plans cat/text/numeric columns from a CSV and generates a runnable training script, and checks parameter sets for the conflicts CatBoost rejects (iterations vs n_estimators, Bayesian bootstrap with subsample, multiple class-weight options, depth > 16, use_best_model without eval_set).
categorical-encoding-selector
Choose and implement the right encoding for every categorical column given the model family and the data: one-hot (with infrequent-level grouping and handle_unknown), ordinal with an explicit order, cross-fitted target encoding (scikit-learn TargetEncoder, CatBoost ordered statistics, category_encoders inside CV), frequency/count encoding, feature hashing, native categorical support in LightGBM, HistGradientBoosting, XGBoost and CatBoost, prefix levels for codes (zip3, NAICS-2), and embeddings. Measures cardinality, rare-level share, unseen-level rate on a time-ordered holdout and target signal per column, emits the ColumnTransformer, and quantifies target-encoding leakage (in-sample vs out-of-fold vs holdout AUC).
classification-model-selector
Choose the right classification algorithm for tabular, text or mixed data and prove the choice with a fair bake-off: logistic regression (L2, L1, elastic net), linear SVM, naive Bayes, k-NN, RBF SVM, decision trees, random forest, extra trees, HistGradientBoosting, LightGBM, XGBoost, CatBoost, explainable boosting machines (EBM/GAM), points scorecards and MLPs, matched to data size, width, categorical and high-cardinality share, missingness, class imbalance, text, and constraints such as interpretability, monotonic constraints, calibrated probabilities, latency budgets and tiny deployable models. Ranks candidates with starting hyperparameters and cautions, then generates a bake-off script running every candidate on identical CV folds with per-fold metrics, fit time and prediction latency.
clustering-algorithm-selector
Choose the right clustering algorithm and check that clustering makes sense at all: cluster tendency (Hopkins statistic with a correlated-features caveat, nearest-neighbour isolation), then k-means / MiniBatchKMeans, Gaussian mixtures with BIC, agglomerative (Ward, average, complete, single) with dendrograms, DBSCAN, HDBSCAN, OPTICS, spectral clustering, BIRCH, mean shift, k-prototypes and k-modes for mixed or categorical data, and Gower distance, matched to data size, expected shapes, noise, varying density, known k, soft memberships, hierarchy and streaming. Covers preprocessing (scaling, encoding, dimensionality, distance metrics), validation (internal indices, bootstrap stability, external labels, business profiling) and assigning new points.
collaborative-filtering-implementer
Implement collaborative filtering recommenders that beat popularity: item-based kNN with cosine, Jaccard or asymmetric similarity, shrinkage, neighbourhood size, positive-only or mean-centred rating signals and 'because you liked X' explanations; implicit ALS with confidence weighting, BPR pairwise ranking and the closed-form EASE model in plain NumPy, tuned on a temporal validation split by recall@k and NDCG@k and refit on all data; plus user-kNN, explicit matrix factorisation, the implicit/LightFM libraries, hyperparameter ranges, cold-start fallbacks, scaling with sparse matrices and approximate nearest neighbours. Produces top-k lists in the format of the recommendation evaluator.