Showing 21-40 of 71 projects
A fast, open-source library for time series forecasting using statistical and econometric models.
A simple and universal swarm intelligence engine for predicting anything, built with Python.
A Python framework for building advanced time series forecasting and anomaly detection models.
A fast library for AutoML and hyperparameter tuning that can be used for various machine learning tasks.
NeuralProphet is a simple forecasting package that uses neural networks and time series analysis to make predictions.
Scalable neural forecasting algorithms for time-series data, with support for various deep learning models.
TimeGPT-1 is a production-ready pre-trained time series foundation model for forecasting and anomaly detection.
A Python library for time series forecasting using LSTM neural networks.
A curated list of awesome resources on transformers in time series analysis, forecasting, and more.
A repository with papers, code, and experiments using deep learning for time series forecasting.
An official implementation of a time series forecasting model using large language models.
An Android app that uses phone sensors to assist with wilderness treks and survival situations.
A collection of Jupyter Notebook tutorials on solving real-world problems with machine learning and deep learning using PyTorch.
An official implementation of a Transformer-based time series forecasting model.
This repository provides an official implementation of a paper on using transformers for time series forecasting.
Autoformer: A deep learning model for long-term time series forecasting, focused on developers building with AI tools.
Official implementation of iTransformer, an effective transformer-based time series forecasting model.
A Python toolkit for building machine/deep learning models on partially-observed time series data
Official implementation of a decomposable multiscale mixing model for time series forecasting
A flexible, intuitive and fast forecasting library for time series data analysis and prediction.
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