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A comprehensive time-series benchmark evaluating state-of-the-art deep learning architectures (PatchTST, TFT, N-HiTS) against traditional gradient boosting (CatBoost) for accurate 24-hour load prediction.
| Date | Stars |
|---|---|
| 2026-07-31 | 1555 |
| 2026-08-04 | 1574 |
| 2026-08-06 | 1578 |
Today
+4 stars today
This week
— stars this week
This month
— stars this month
Momentum
21.0
growth rate 0.00%/day
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<h1>Прогнозирование нагрузки на каждый час суток с помощью PatchTST и Temporal Fusion Transformer: сравнительный системный анализ с традиционными методами бустинга (CatBoost) и нейросетевыми моделями на базе N-HiTS</h1>
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Информационная система для сравнительного анализа моделей прогнозирования почасовой нагрузки: PatchTST, Temporal Fusion Transformer (TFT), N-HiTS и CatBoost. Включает предобработку данных, обучение моделей, валидацию и визуализацию результатов для поддержки принятия решений на энергорынках.
</p>
<p>
<a href="https://github.com/KEV0143/Comparative-analysis-of-hourly-load-forecasting-using-PatchTST-TFT-NHiTS-and-CatBoost/releases/latest"><img src="https://img.shields.io/github/v/release/KEV0143/Comparative-analysis-of-hourly-load-forecasting-using-PatchTST-TFT-NHiTS-and-CatBoost?style=flat-square&color=blue" alt="Latest Release"></a>
<a href="https://github.com/KEV0143/Comparative-analysis-of-hourly-load-forecasting-using-PatchTST-TFT-NHiTS-and-CatBoost/releases"><img src="https://img.shields.io/badge/Platform-Windows%20%7C%20Linux-lightgrey?style=flat-square" alt="Supported Platforms"></a>
<a href="https://github.com/KEV0143/Comparative-analysis-of-hourly-load-forecasting-using-PatchTST-TFT-NHiTS-and-CatBoost/blob/main/LICENSE"><img src="https://img.shields.io/badge/License-Apache_2.0-blue?style=flat-square" alt="License"></a>
<br>
<a href="https://github.com/KEV0143/Comparative-analysis-of-hourly-load-forecasting-using-PatchTST-TFT-NHiTS-and-CatBoost/pulse"><img src="https://img.shields.io/github/release-date/KEV0143/Comparative-analysis-of-hourly-load-forecasting-using-PatchTST-TFT-NHiTS-and-CatBoost?style=flat-square" alt="Release Date"></a>
<a href="https://github.com/KEV0143/Comparative-analysis-of-hourly-load-forecasting-using-PatchTST-TFT-NHiTS-and-CatBoost/search?l=python"><img src="https://img.shields.io/github/languages/code-size/KEV0143/Comparative-analysis-of-hourly-load-forecasting-using-PatchTST-TFT-NHiTS-and-CatBoost?style=flat-square" alt="Code Size"></a>
<a href="https://github.com/KEV0143/Comparative-analysis-of-hourly-load-forecasting-using-PatchTST-TFT-NHiTS-and-CatBoost/stargazers"><img src="https://img.shields.io/github/stars/KEV0143/Comparative-analysis-of-hourly-load-forecasting-using-PatchTST-TFT-NHiTS-and-CatBoost?style=flat-square&color=yellow" alt="Stars"></a>
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<p style="font-size: 1.1em;">
<a href="https://github.com/KEV0143/Comparative-analysis-of-hourly-load-forecasting-using-PatchTST-TFT-NHiTS-and-CatBoost/blob/main/README.md" style="text-decoration: none;">
<b>Русская локализация</b>
</a>
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<a href="https://github.com/KEV0143/Comparative-analysis-of-hourly-load-forecasting-using-PatchTST-TFT-NHiTS-and-CatBoost/blob/main/README.EN.md" style="text-decoration: none;">
<b>English Localization</b>
</a>
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<a href="https://github.com/KEV0143/Comparative-analysis-of-hourly-load-forecasting-using-PatchTST-TFT-NHiTS-and-CatBoost/blob/main/README.zh-CN.md" style="text-decoration: none;">
<b>中文本地化</b>
</a>
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<p>
<img
src="https://img.shields.io/badge/Python-3.11-blue?style=flat-square&logo=python&logoColor=white"
alt="Python 3.11"
>
<img
src="https://img.shields.io/badge/PyTorch-2.13-EE4C2C?style=flat-square&logo=pytorch&logoColor=white"
alt="PyTorch 2.13"
>
<img
src="https://img.shields.io/badge/Darts-Time%20Series-orange?style=flat-square"
alt="Darts"
>
<img
src="https://img.shields.io/badge/CatBoost-Gradient%20Boosting-yellow?style=flat-square"
alt="CatBoost"
>
<img
src="https://img.shields.io/badge/Pandas-Data%20Processing-150458?style=flat-square&logo=pandas&logoColor=white"
alt="Pandas"
>
</p>
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<p align="center">
<b>А. Э. ДзгоExcerpt of 55,507 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:b5e5b8b220de3e1c, llm:description: time-series benchmark evaluating deep learning architectures (PatchTST, TFT, N-HiTS) and CatBoost for 24-hour load prediction
matched fp:b5e5b8b220de3e1c, llm:description: time-series benchmark evaluating deep learning architectures (PatchTST, TFT, N-HiTS) and CatBoost for 24-hour load prediction
matched fp:b5e5b8b220de3e1c, llm:description: time-series benchmark evaluating deep learning architectures (PatchTST, TFT, N-HiTS) and CatBoost for 24-hour load prediction