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Basic RNN, LSTM, GRU, and Attention for time-series prediction

Beginner-friendly collection of Python notebooks for various use cases of machine learning, deep learning, and analytics. For each notebook there is a separate tutorial on the relataly.com blog.

Online-Recurrent-Extreme-Learning-Machine (OR-ELM) for time-series prediction, implemented in python

This repository is designed to teach you, step-by-step, how to develop deep learning methods for time series forecasting with concrete and executable examples in Python.

Building Time series forecasting models, including the XGboost Regressor, GRU (Gated Recurrent Unit), LSTM (Long Short-Term Memory), CNN (Convolutional Neural Network), CNN-LSTM, and LSTM-Attention. Additionally, hybrid models like GRU-XGBoost and LSTM-Attention-XGBoost for Electricity Demand and... Read more

Conformal prediction for time-series applications.

TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series, from ServiceNow Research

This repository contains a throughout explanation on how to create different deep learning models in Keras for multivariate (tabular) time series prediction.

Official code for: Conformal prediction interval for dynamic time-series (conference, ICML 21 Long Presentation) AND Conformal prediction for time-series (journal, IEEE TPAMI)

Transformer based model for time series prediction