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SkillsMP は skforecast/skforecast-ai から 16 件の skill を収集しています。skill を開くとソースと詳細を確認できます。
収集済み skill 16 件中 16 件を表示しています。
Configures TimeSeriesFold parameters for backtesting based on deployment scenarios. Maps business requirements (retraining frequency, forecast horizon, data budget) to cross-validation strategy parameters. Use when the user describes how they plan to deploy…
原文の言語: 英語
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…
原文の言語: 英語
Complete constructor signatures and method signatures for all skforecast forecasters, backtesting functions, search functions, cross-validation classes, preprocessing, feature selection, and drift detection. Use when the user needs exact parameter names,…
原文の言語: 英語
Forecasts time series using recurrent neural networks (RNN, LSTM, GRU) with ForecasterRnn and the create_and_compile_model helper. Covers model architecture, training, and multi-series deep learning. Use when the user wants to use deep learning / neural…
原文の言語: 英語
Creates features for time series forecasting: calendar features with skforecast's `CalendarFeatures` (cyclical, onehot, or spline encoding) — either delegated to the forecaster via the `calendar_features` parameter or built manually as exog — holiday distance…
原文の言語: 英語
Forecasts multiple time series simultaneously using a global model with ForecasterRecursiveMultiSeries or ForecasterDirectMultiVariate. Covers data formats, encoding, per-series transformers, and multi-series backtesting. Use when the user has two or more…
原文の言語: 英語
Forecasts a single time series using ForecasterRecursive or ForecasterDirect. Covers data preparation, model creation, training, prediction, backtesting, and prediction intervals. Use when the user needs to predict future values of one time series.
原文の言語: 英語
Zero-shot time series forecasting with pre-trained foundation models (Amazon Chronos-2, Google TimesFM 2.5, Salesforce Moirai-2, Soda-INRIA TabICL, Prior Labs TabPFN-TS, The Forecasting Company T0) via ForecasterFoundation and FoundationModel. Covers single…
原文の言語: 英語
Optimizes forecaster hyperparameters using grid search, random search, or Bayesian search (Optuna). Covers single-series and multi-series search, cross-validation configuration, and search space definition. Use when the user wants to find the best model…
原文の言語: 英語
Generates prediction intervals for time series forecasts using bootstrapping, conformal prediction, or built-in statistical model intervals. Covers interval configuration, residual management, and calibration. Use when the user needs uncertainty…
原文の言語: 英語
Forecasts time series using classical statistical models (ARIMA, SARIMAX, ETS, ARAR) wrapped in ForecasterStats. Covers model selection, Auto-ARIMA, backtesting statistical models, and parameter tuning. Use when the user wants traditional statistical…
原文の言語: 英語
Diagnoses and fixes common errors when using skforecast, especially mistakes frequently made by LLMs generating skforecast code. Covers deprecated imports, wrong function names, missing parameters, and data format issues. Use when generated code produces…
原文の言語: 英語
Guides selection of the most appropriate evaluation metric(s) for a forecasting task based on forecaster type, prediction output type, data characteristics, and multi-series aggregation needs. Use when the user asks which metric to use, how to evaluate…
原文の言語: 英語
Analyzes time series dynamics with the fast skforecast.stats functions acf, pacf and calculate_lag_autocorrelation. Covers reading ACF/PACF patterns to identify AR/MA orders and seasonality, ranking lags by partial autocorrelation, and feeding the result to…
原文の言語: 英語
Detects data drift in time series forecasting pipelines using RangeDriftDetector and PopulationDriftDetector. Covers range-based out-of-range detection and statistical distribution tests. Use when the user wants to monitor model reliability in production.
原文の言語: 英語
Selects the most relevant lags, window features, and exogenous variables using sklearn feature selectors (RFECV, SelectFromModel). Covers single-series and multi-series selection with force inclusion and subsampling. Use when the user has many features and…
原文の言語: 英語