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Hibra999

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• 39 skills
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• 18 hours ago last updated

1. ▌
   Skforecast · hibra999 bundle

   Use skforecast 0.22+ for time-series forecasting after forecasting-data-prep, including pandas Series/DataFrame inputs, ForecasterRecursive, ForecasterDirect, ForecasterRecursiveMultiSeries, ForecasterDirectMultiVariate, ForecasterRecursiveClassifier, ForecasterRnn, ForecasterStats, ForecasterFoundation, ForecasterEquivalentDate, scikit-learn-compatible estimators, ARIMA/SARIMAX/AutoARIMA/ETS/AutoETS/ARAR, exogenous variables, window features, backtesting, hyperparameter search, prediction intervals, conformal/quantile forecasts, plotting, explainability, drift detection, and anti-leakage validation.
   0 installs
2. ▌
   Anomaly Pyod · hibra999 bundle

   Use PyOD for anomaly/outlier detection after validating prepared tabular or time-indexed feature matrices, including classic fit/predict detectors, time-series detectors, ADEngine selection, contamination thresholds, anomaly scores, labels, probabilities, evaluation metrics, plotting, model persistence, and anti-leakage safeguards.
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3. ▌
   Anomaly Tods · hibra999 bundle

   Use TODS for automated time-series outlier detection after validating multivariate time-series data, including D3M pipeline primitives, default pipeline evaluation, AutoML pipeline search, point-wise, pattern-wise, and system-wise detection, PyOD wrappers, DeepLog, Telemanom, MatrixProfile, feature extraction, metrics, and anti-leakage safeguards.
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4. ▌
   Statsforecast · hibra999 bundle

   Use Nixtla StatsForecast for fast statistical and econometric forecasting after forecasting-data-prep, including long-format pandas/polars data with unique_id/ds/y, local models for many series, AutoARIMA/AutoETS/AutoCES/AutoTheta/AutoMFLES/AutoTBATS, ARIMA, AutoRegressive, Theta, MSTL, MFLES, TBATS, GARCH/ARCH, exponential smoothing, naive/intermittent baselines, exogenous regressors, static covariates, conformal or native prediction intervals, cross_validation, distributed Dask/Ray/Spark workflows, plotting, fitted values, and leakage-safe temporal validation.
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5. ▌
   Aggregation Kats · hibra999 bundle

   Use Facebook/Meta Kats TSFeatures for time-series aggregation and feature extraction after validating data, including TimeSeriesData inputs, univariate and multivariate feature dictionaries, multiple independent series loops, feature groups, opt-in/opt-out feature selection, tabular ML matrices, plotting context, and leakage-safe train/fold feature generation.
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6. ▌
   Changepoint Adtk · hibra999 bundle

   Use Arundo ADTK for changepoint-like anomaly event detection after validating prepared pandas time series, including LevelShiftAD, PersistAD, VolatilityShiftAD, SeasonalAD, AutoregressionAD, threshold/outlier detectors, multivariate detectors, event conversion, metrics, plotting, temporal validation, and anti-leakage safeguards.
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7. ▌
   Changepoint Kats · hibra999 bundle

   Use Kats for changepoint, level-shift, online Bayesian changepoint, robust statistical changepoint, rolling CUSUM, trend, and statistical-change detection after validating prepared time-series data, including TimeSeriesData inputs, univariate/multivariate constraints, thresholds, windows, priors, evaluation, plotting, and leakage-safe offline or online workflows.
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8. ▌
   Etna Forecasting · hibra999 bundle

   Use ETNA 3.0 forecasting after forecasting-data-prep, including TSDataset long/wide pandas data, single or multiple segments, exogenous regressors with df_exog and known_future, Pipeline and AutoRegressivePipeline workflows, statistical/ML/neural/pretrained models, ensembles, hierarchical pipelines, prediction intervals, temporal backtesting, metrics, plotting, residual diagnostics, and anti-leakage safeguards.
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9. ▌
   Kats Forecasting · hibra999 bundle

   Use Facebook/Meta Kats for forecasting with TimeSeriesData, classical models, Prophet, LSTM, LightGBM MLAR, global models, ensembles, temporal hierarchical reconciliation, hyperparameter/meta-learning helpers, prediction intervals where documented, plotting, backtesting, and leakage-safe temporal validation. Trigger when an agent needs to model prepared time-series data with Kats after applying forecasting-data-prep for frequency, horizon, splits, covariates, and anti-leakage checks, especially when working with legacy Kats 0.2.0 projects.
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10. ▌
    Aggregation Tsfel · hibra999 bundle

    Use TSFEL for time-series aggregation and feature extraction after validating data, including ndarray/Series/DataFrame inputs, univariate and multivariate signals, window_size and overlap extraction, statistical/temporal/spectral/fractal domains, JSON feature configs, dataset_features_extractor file workflows, custom features, sampling-frequency checks, tabular ML matrices, and leakage-safe train/fold feature generation.
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11. ▌
    Anomaly Luminaire · hibra999 bundle

    Use Luminaire for time-series anomaly and outlier detection after validating ordered univariate data, including DataExploration profiling, HyperparameterOptimization, LADStructuralModel, LADFilteringModel, WindowDensityModel streaming/window detection, anomaly probabilities, confidence intervals, model freshness, and anti-leakage safeguards.
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12. ▌
    Clustering Stumpy · hibra999 bundle

    Use STUMPY for time-series pattern search, motif discovery, matrix profiles, nearest-neighbor subsequence search, query matching with stumpy.match or stumpy.mass, motif discovery with stump/motifs, multidimensional motifs with mstump/mmotifs, MPdist similarity for clustering, snippets, semantic segmentation, shapelet discovery, streaming matrix profiles, Dask/Ray/GPU scaling, and leakage-aware time-series pattern validation.
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13. ▌
    Darts Forecasting · hibra999 bundle

    Use Unit8 Darts for forecasting with TimeSeries, local/statistical models, global regression and deep learning models, foundation models, covariates, static covariates, probabilistic forecasts, conformal prediction, multiple-series training, backtesting, residual diagnostics, plotting, and leakage-safe temporal validation. Trigger when an agent needs to model prepared time-series data with Darts after applying forecasting-data-prep for frequency, horizon, covariate availability, splits, and anti-leakage checks.
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14. ▌
    Fedot Forecasting · hibra999 bundle

    Use FEDOT 0.7.5 for AutoML time-series forecasting after forecasting-data-prep, including Fedot(problem='ts_forecasting'), TaskTypesEnum.ts_forecasting, TsForecastingParams, InputData time-series loaders, univariate forecasting, multi-time-series and multimodal exogenous workflows, lagged/sparse_lagged/exog_ts transformations, native TS and regression operations, forecast_length/horizon handling, temporal train_test_data_setup, cv_folds validation, metrics, plotting, residual checks, and anti-leakage safeguards.
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15. ▌
    Aggregation Tsflex · hibra999 bundle

    Use tsflex for time-series aggregation and feature extraction after validating data, including pandas Series/DataFrame/list inputs, wide or series-list layouts, asynchronous multivariate signals, irregular sampling, strided rolling windows, FeatureCollection/FeatureDescriptor/MultipleFeatureDescriptors, FuncWrapper custom features, external feature integrations, chunking gaps, execution logging, serialization, and leakage-safe tabular ML feature generation.
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16. ▌
    Sktime Forecasting · hibra999 bundle

    Use sktime for forecasting with its unified forecaster API, ForecastingHorizon, temporal splitters, pipelines, tuning, reductions to regression, statistical/deep/foundation/wrapped forecasters, exogenous variables, probabilistic forecasts, panel/global/hierarchical data, and leakage-safe backtesting. Trigger when an agent needs to model prepared time-series data with sktime after applying forecasting-data-prep for frequency, horizon, splits, covariates, and anti-leakage checks.
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17. ▌
    Aggregation Tsfresh · hibra999 bundle

    Use tsfresh for time-series aggregation and feature extraction after validating data, including flat, stacked, and dict input formats, feature calculator settings, supervised relevance filtering, sklearn transformers, Dask/Spark scaling, rolling windows, and leakage-safe tabular ML feature generation.
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18. ▌
    Changepoint Luminol · hibra999 bundle

    Use LinkedIn Luminol for changepoint-like anomaly event detection after validating prepared time-series data, including AnomalyDetector anomaly windows, exact anomaly timestamps, bitmap/derivative/EMA/threshold/sign-test algorithms, baseline comparisons, Correlator root-cause ranking, temporal evaluation, plotting outside Luminol, and anti-leakage safeguards.
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19. ▌
    Changepoint Merlion · hibra999 bundle

    Use Salesforce Merlion for native Bayesian online changepoint detection after validating prepared TimeSeries data, including BOCPD, LevelShift/TrendChange/Auto change kinds, univariate or multivariate inputs, online updates, z-score changepoint scores, thresholded alarms, TSAD metrics, plotting, and leakage-safe evaluation.
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20. ▌
    Changepoint Prophet · hibra999 bundle

    Use Prophet for trend changepoint analysis inside Prophet forecasting workflows after validating prepared time-series data, including ds/y pandas inputs, automatic or manual potential changepoints, changepoint prior tuning, trend deltas, forecast intervals, cross-validation, plotting significant changepoints, regressors, holidays/shocks, and leakage-safe retrospective or forecasting use.
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21. ▌
    Classification Etna · hibra999 bundle

    Use ETNA experimental time-series classification after ts-classification-data-prep, including etna[classification], TimeSeriesBinaryClassifier, TSFreshFeatureExtractor, WEASELFeatureExtractor, sklearn-compatible binary classifiers, PredictabilityAnalyzer, UCR-style 2D arrays or lists of 1D series, TSDataset segment predictability, masked cross-validation, predict/predict_proba, and anti-leakage safeguards.
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22. ▌
    Classification Pyts · hibra999 bundle

    Use pyts 0.13.0 for time-series classification after ts-classification-data-prep, including univariate 2D arrays shaped (n_samples, n_timestamps), multivariate 3D arrays shaped (n_samples, n_features, n_timestamps), KNeighborsClassifier with DTW/BOSS metrics, SAXVSM, BOSSVS, LearningShapelets, TimeSeriesForest, TSBF, MultivariateClassifier, WEASELMUSE pipelines, pyts preprocessing and transformations, fit/predict/predict_proba where documented, stratified validation, and anti-leakage safeguards.
    0 installs
23. ▌
    Merlion Forecasting · hibra999 bundle

    Use Salesforce Merlion for forecasting workflows after forecasting-data-prep, including TimeSeries/UnivariateTimeSeries data, DefaultForecaster, Arima, Sarima, ETS, Prophet, MSES, VectorAR, tree forecasters, DeepAR/Autoformer/ETSformer/Informer/Transformer, AutoETS/AutoProphet/AutoSarima, ForecasterEnsemble, exogenous regressors, uncertainty/error bars, ForecastEvaluator live-deployment validation, forecast metrics, plotting, and anti-leakage safeguards.
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24. ▌
    Prophet Forecasting · hibra999 bundle

    Use Prophet for univariate forecasting with trend, seasonality, holidays, extra regressors, uncertainty intervals, temporal cross-validation, and diagnostic plots. Trigger this skill when an agent needs to model a prepared time-series dataset with the official Python or R Prophet library, after first applying forecasting-data-prep to validate frequency, horizon, covariate availability, temporal splits, and anti-leakage safeguards.
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25. ▌
    Pytorch Forecasting · hibra999 bundle

    Use sktime/PyTorch Forecasting for neural time-series forecasting with pandas DataFrames, TimeSeriesDataSet, Lightning Trainer, TemporalFusionTransformer, DeepAR/DeepVAR, N-BEATS, N-HiTS, TiDE, TimeXer, xLSTMTime, RecurrentNetwork, DecoderMLP, Baseline, probabilistic or quantile losses, covariates, multiple series via group_ids, multiple targets, temporal validation, plotting, interpretation, and anti-leakage safeguards after applying forecasting-data-prep.
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26. ▌
    Changepoint Greykite · hibra999 bundle

    Use LinkedIn Greykite for offline long-term changepoint analysis after validating prepared time-series data, including pandas time/value inputs, ChangepointDetector adaptive-lasso trend changepoints, seasonality changepoints, Silverkite changepoints configuration, level-shift regressors, forecast backtests, plotting, parameter tuning, and anti-leakage safeguards.
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27. ▌
    Changepoint Ruptures · hibra999 bundle

    Use ruptures for offline change point detection and signal segmentation after validating ordered time-series data, including univariate/multivariate numpy signals, exact and approximate search methods, cost models, known or unknown breakpoint counts, penalties, segmentation metrics, plotting, custom costs, and leakage-safe offline evaluation.
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28. ▌
    Greykite Forecasting · hibra999 bundle

    Use LinkedIn Greykite for interpretable univariate forecasting after forecasting-data-prep, including pandas DataFrame inputs with time/value/regressor columns, Forecaster.run_forecast_config, ForecastConfig, MetadataParam, Silverkite, Prophet, Auto-ARIMA, lag-based and multistage templates, AUTO/SILVERKITE model templates, holidays/events, changepoints, regressors, lagged regressors, autoregression, prediction intervals via coverage, rolling time-series CV/backtest, benchmarking, plotting, component diagnostics, and anti-leakage safeguards.
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29. ▌
    Classification Sktime · hibra999 bundle

    Use sktime for time-series classification after ts-classification-data-prep, including Panel data formats such as numpy3D, pd-multiindex, df-list, nested_univ, equal/unequal length handling, univariate and multivariate samples, distance/kernel, dictionary, interval, feature, shapelet, ROCKET, hybrid, ensemble, early, deep learning and foundation classifiers, ClassifierPipeline, SklearnClassifierPipeline, predict/predict_proba, stratified cross-validation, model selection, padding/truncation, and anti-leakage safeguards.
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30. ▌
    Forecasting Data Prep · hibra999 bundle

    Prepare, validate, diagnose, and split time-series datasets before forecasting or other ordered time-series work, then write problem-summary.txt for skill routing. Use this skill whenever an agent must define the problem and dataset, identify time and target columns, validate frequency and panel structure, handle gaps/missing values/outliers/exogenous variables, create temporal splits/backtests, and prevent data leakage before modeling, detection, discovery, or aggregation.
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31. ▌
    Classification Tslearn · hibra999 bundle

    Use tslearn 0.8.1 for time-series classification after ts-classification-data-prep, including 3D arrays shaped (n_ts, sz, d), variable-length NaN padding via to_time_series_dataset, univariate and multivariate samples, KNeighborsTimeSeriesClassifier, TimeSeriesSVC, LearningShapelets, TimeSeriesMLPClassifier, NonMyopicEarlyClassifier, tslearn preprocessing, fit/predict/predict_proba, stratified validation, and anti-leakage pipelines.
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32. ▌
    Classification Dl 4 Tsc · hibra999 bundle

    Use hfawaz/dl-4-tsc for time-series classification after ts-classification-data-prep, including UCRArchive_2018 TSV files, MTS .npy arrays, fixed 3D tensors shaped (n_samples, n_timestamps, n_variables), TensorFlow/Keras deep classifiers, main.py experiment runs, metrics/logs, plotting artifacts, and leakage-aware evaluation.
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33. ▌
    Statsmodels Forecasting · hibra999 bundle

    Use Statsmodels for classical and econometric forecasting with AR/ARIMA/SARIMAX, exponential smoothing/ETS, state space models, VAR/SVAR/VECM/VARMAX, dynamic factor models, ARDL/UECM, ThetaModel, STLForecast, exogenous regressors, prediction intervals, temporal validation, and residual diagnostics. Trigger when an agent needs to model prepared time-series data with official statsmodels APIs after applying forecasting-data-prep for frequency, horizon, splits, covariates, and anti-leakage checks.
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34. ▌
    Changepoint Alibi Detect · hibra999 bundle

    Use Seldon Alibi Detect for changepoint-like distribution-change monitoring after validating prepared time-series arrays, including online MMD/LSDD/CVM/FET drift detectors, rolling offline drift tests, reference windows, ERT, window sizes, preprocessing/backends, state handling, evaluation delay, plotting, and anti-leakage safeguards.
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35. ▌
    Time Series Skill Router · hibra999 bundle

    Read a validated problem-summary.txt and skills.catalog.yaml to recommend the smallest ordered sequence of repository skills for forecasting, classification, pattern discovery, feature aggregation, change point detection, or anomaly detection. Use this skill after a data-preparation skill has documented the problem, dataset, variables, validation plan, constraints, readiness, and leakage risks, or whenever an agent must choose and order skills from this repository without redundant library alternatives.
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36. ▌
    TS Classification Data Prep · hibra999 bundle

    Prepare, validate, split, and document time-series classification datasets, then write problem-summary.txt for skill routing. Use this skill whenever an agent must define the classification problem, samples, labels, tensor or panel shape, channel order, padding or truncation policy, split IDs, class balance, group or time leakage risks, and preprocessing fit boundaries before classifier selection.
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37. ▌
    Changepoint Time Series Library · hibra999 bundle

    Use THUML Time-Series-Library for process-change event detection through its documented anomaly_detection task after validating prepared multivariate time-series data, including supported PSM/MSL/SMAP/SMD/SWAT loaders, run.py scripts, reconstruction-error thresholds, anomaly_ratio, event-level adjustment, Accuracy/Precision/Recall/F-score, and leakage-safe conversion of anomaly intervals to changepoint-like starts or ends.
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38. ▌
    Time Series Library Forecasting · hibra999 bundle

    Use THUML Time-Series-Library (TSLib) for deep-learning time-series forecasting experiments with run.py, long_term_forecast, short_term_forecast, zero_shot_forecast, ETT/custom/M4 loaders, M/S/MS feature modes, seq_len/label_len/pred_len horizons, TimeXer exogenous workflows, deterministic metrics, temporal splits, and leakage-safe validation. Trigger when an agent needs to train, evaluate, or adapt a prepared forecasting dataset with TSLib after applying forecasting-data-prep.
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39. ▌
    Classification Time Series Library · hibra999 bundle

    Use THUML Time-Series-Library/TSLib for deep learning time-series classification after ts-classification-data-prep, including UEA .ts TRAIN/TEST files, UEAloader, variable-length padding masks, multivariate channels, task_name=classification, CrossEntropyLoss training, softmax probabilities, accuracy evaluation, official classification scripts, supported model names such as TimesNet, Transformer, Autoformer, PatchTST, iTransformer, MambaSingleLayer/MambaSL, TimeMixer, and strict anti-leakage preprocessing.
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