Fast and Accurate ML in 3 Lines of Code
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The GitHub repository for the paper "Informer" accepted by AAAI 2021.
- deep-learning
- self-attention
- time-series
- pytorch
Chronos: Pretrained Models for Time Series Forecasting
- forecasting
- large-language-models
- llm
- machine-learning
Time series Timeseries Deep Learning Machine Learning Python Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai
- time-series-classification
- deep-learning
- fastai
- pytorch
Lightning ⚡️ fast forecasting with statistical and econometric models.
- time-series
- statistics
- forecasting
- arima
Scalable and user friendly neural :brain: forecasting algorithms.
- deep-learning
- forecasting
- esrnn
- nbeats
NeuralProphet: A simple forecasting package
- forecasting
- time-series
- machine-learning
- fbprophet
TimeGPT-2.1: 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 🚀.
- time-series
- time-series-forecasting
- deep-learning
- forecasting
An offical implementation of PatchTST: "A Time Series is Worth 64 Words: Long-term Forecasting with Transformers." (ICLR 2023) https://arxiv.org/abs/2211.14730
[AAAI-23 Oral] Official implementation of the paper "Are Transformers Effective for Time Series Forecasting?"
- deep-learning
- forecasting
- forecasting-model
- linear-models
About Code release for "Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting" (NeurIPS 2021), https://arxiv.org/abs/2106.13008
Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).
- deep-learning
- pytorch
- time-series-forecasting
- time-series
Official implementation for "iTransformer: Inverted Transformers Are Effective for Time Series Forecasting" (ICLR 2024 Spotlight)
- time-series-forecasting
- transformer
Predict stock market prices using RNN model with multilayer LSTM cells + optional multi-stock embeddings.
- lstm
- rnn-tensorflow
- stock-price-prediction
- embeddings
1st place solution
- kaggle-web-traffic
- kaggle
- time-series
- timeseries
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.
[PVLDB 2024 Best Paper Nomination] TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods
- benchmarking
- time-series
- time-series-forecasting
- deep-learning
Unified Training of Universal Time Series Forecasting Transformers
- deep-learning
- forecasting
- machine-learning
- pre-trained-models
Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
- forecasting
- foundation-models
- time-series
- time-series-forecasting
The fast Continuous Wavelet Transform (fCWT) is a library for fast calculation of CWT.
- cwt
- matlab
- cpp
- optimization
A portable C99 library of feature extraction functions for time series and other numeric data
Stock price prediction using a deterministic algorithm inspired by LSTM, focusing on pattern recognition in historical data.