TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.
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A Library for Advanced Deep Time Series Models for General Time Series Analysis.
Fast and Accurate ML in 3 Lines of Code
Automatic extraction of relevant features from time series:
- data-science
- feature-extraction
- time-series
Lightning ⚡️ fast forecasting with statistical and econometric models.
- time-series
- statistics
- forecasting
- arima
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
- pytorch
- graph-neural-networks
- temporal-networks
- temporal-graphs
A flexible, intuitive and fast forecasting library
Python library for time series forecasting using scikit-learn compatible models, statistical methods, and foundation models
- forecasting
- scikit-learn
- machine-learning
- python
ObsPy: A Python Toolbox for seismology/seismological observatories.
- seismology
- python
- observatories
- science
Tidy time series forecasting
- forecasting
Exponentially Weighted Moving Average algorithms for Go.
The fast Continuous Wavelet Transform (fCWT) is a library for fast calculation of CWT.
- cwt
- matlab
- cpp
- optimization
Probabilistic programming framework that facilitates objective model selection for time-varying parameter models.
- time-series-analysis
- model-selection
- bayesian-inference
- anomaly-detection
NitroFE is a Python feature engineering engine which provides a variety of modules designed to internally save past dependent values for providing continuous calculation.
- feature-engineering
- machine-learning
- feature
- features
Streaming-first technical indicators with a Rust core: native Python, Node.js and WebAssembly bindings plus a C ABI hub for C, C++, C#, Go, Java and R. 514 indicators, O(1) per-tick updates, no system dependencies. Drop-in TA-Lib replacement.
- algotrading
- backtesting
- bollinger-bands
- cryptocurrency
A dependency-free Linear Algebra, Statistics, and Machine Learning library written from scratch in V