TimeGPT-1: production ready pre-trained Time Series Foundation Model for forecasting and anomaly detection. Generative pretrained transformer for time series trained on over 100B data points. It's capable of accurately predicting various domains such as retail, electricity, finance, and IoT with just a few lines of code 🚀.

👖 Conformal Tights adds conformal prediction of coherent quantiles and intervals to any scikit-learn regressor or Darts forecaster

Resources about time series forecasting and deep learning.

Boosted Hybrids of ensemble gradient algorithm for the long-term time series forecasting (LTSF)

This is a repository for collecting papers and code in time series domain.

Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting

TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods (PVLDB 2024) https://arxiv.org/pdf/2403.20150.pdf

[ICML2024] Unified Training of Universal Time Series Forecasting Transformers

Analise dos retornos da companhia aerea LATAM na bolsa chilena usando series temporais . Esse trabalho é o projeto final da disciplina ME607-Series Temporais na UNICAMP

This repository contains a reading list of papers on Time Series Forecasting/Prediction (TSF) and Spatio-Temporal Forecasting/Prediction (STF). These papers are mainly categorized according to the type of model.

A curated list of projects based on applications of Machine learning and Actuarial Science.

To develop an advance forecasting model that adeptly incorporates solar irradiance data, leveraging its predictive capabilities to elevate forecasting performance and reliability.

Python Client and Toolkit for DataFrames, Big Data, Machine Learning and ETL in Elasticsearch

Using prophet forecasting to predict individual household power consumption

Official implementation for "iTransformer: Inverted Transformers Are Effective for Time Series Forecasting" (ICLR 2024 Spotlight), https://openreview.net/forum?id=JePfAI8fah

A time series forecasting ensemble model designed to provide robust predictions by combining the strengths of multiple forecasting learners.

Projeto para previsão de séries temporais utilizando modelos de machine learning. O projeto implementa um grid search para otimização de hiperparâmetros e possibilita a utilização de três arquiteturas de rede neural: MLP, LSTM e BiLSTM. Os dados utilizados são referentes aos preços de commodities agrícolas, obtidos de fontes públicas.

Chronos: Pretrained (Language) Models for Probabilistic Time Series Forecasting

MOMENT: A Family of Open Time-series Foundation Models

Time-Series Work Summary in CS Top Conferences (NIPS, ICML, ICLR, KDD, AAAI, WWW, IJCAI, CIKM, ICDM, ICDE, etc.)