nhits

Scalable and user friendly neural 🧠 forecasting algorithms.

Awesome Easy-to-Use Deep Time Series Modeling based on PaddlePaddle, including comprehensive functionality modules like TSDataset, Analysis, Transform, Models, AutoTS, and Ensemble, etc., supporting versatile tasks like time series forecasting, representation learning, and anomaly detection, etc., featured with quick tracking of SOTA deep models.

This repository is the implementation of the paper: ViT2 - Pre-training Vision Transformers for Visual Times Series Forecasting. ViT2 is a framework designed to address generalization & transfer learning limitations of Time-Series-based forecasting models by encoding the time-series to images using GAF and a modified ViT architecture.

Forecasting daily total sales 🧾 of different gifting items 🎁 using holiday data 🎄, promotional sales data 🏷️ , and other time-series features 🕛

Day-ahead electricity price forecasting for the German bidding zone, under a strict gate-closure information constraint. The most accurate model turns out to be one that cannot run.