KEV0143/Comparative-analysis-of-hourly-load-forecasting-using-PatchTST-TFT-NHiTS-and-CatBoost resource

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1676 stars · 0 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A Python benchmark project comparing deep learning time-series architectures (PatchTST, Temporal Fusion Transformer, N-HiTS) against CatBoost gradient boosting for 24-hour electricity load forecasting. It includes data preprocessing, model training, validation, and result visualization for energy market decision support.

Use cases

• compare deep learning vs gradient boosting for hourly load forecasting
• benchmark PatchTST against TFT and N-HiTS on time-series data
• predict electricity demand 24 hours ahead
• evaluate CatBoost for time-series regression
• find the best model architecture for energy load prediction
• learn how to run forecasting experiments with multiple models

When to choose

• you need a ready-made comparison of modern time-series architectures for load forecasting
• you work in energy analytics and need hourly demand predictions
• you want a reference implementation of PatchTST, TFT, N-HiTS, and CatBoost in one pipeline

When to avoid

• you need a production forecasting service rather than a research benchmark
• your forecasting problem is unrelated to hourly energy load
• you need a maintained library with an API instead of an analysis codebase

Facets

learning-resource · maturity active

machine-learning benchmarking data-science data-visualization etl machine-learning data-science time-series energy analytics python windows time-series-forecasting load-forecasting patchtst temporal-fusion-transformer n-hits catboost deep-learning energy-markets benchmark linux

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