Statsmodels: statistical modeling and econometrics in Python
- python
- statistics
- econometrics
- data-analysis
Repository Index
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A unified framework for machine learning with time series
- time-series
- machine-learning
- scikit-learn
- time-series-classification
Numenta Platform for Intelligent Computing is an implementation of Hierarchical Temporal Memory (HTM), a theory of intelligence based strictly on the neuroscience of the neocortex.
- neocortex
- artificial-intelligence
- machine-intelligence
- hierarchical-temporal-memory
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
Technical Analysis Library using Pandas and Numpy
- trading
- technical-analysis
- python3
- volume
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
Python AutoML toolkit for benchmarking classification, regression, and time-series forecasting models, with GPU acceleration, MLflow tracking, and automated preprocessing.
- machine-learning
- automl
- regression
- classification
The machine learning toolkit for time series analysis in Python
- time-series
- timeseries
- time-series-analysis
- time-series-clustering
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
Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).
- deep-learning
- pytorch
- time-series-forecasting
- time-series
A Python toolkit/library for reality-centric machine/deep learning & data mining on partially-observed time series, with 50+ SOTA neural network models for scientific analysis tasks (imputation, classification, clustering, forecasting, anomaly detection, cleaning) on incomplete industrial irregularly-sampled multivariate TS with NaN missing values
- time-series
- data-mining
- deep-learning
- missing-values
A Python package for time series classification
- python
- classification
- machine-learning
- time-series-classification
[PVLDB 2024 Best Paper Nomination] TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods
- benchmarking
- time-series
- time-series-forecasting
- deep-learning
A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function.
- arima
- time-series
- forecasting
- forecasting-models
Python library for time series forecasting using scikit-learn compatible models, statistical methods, and foundation models
- forecasting
- scikit-learn
- machine-learning
- python
Supply a wrapper ``StockDataFrame`` based on the ``pandas.DataFrame`` with inline stock statistics/indicators support.
ObsPy: A Python Toolbox for seismology/seismological observatories.
- seismology
- python
- observatories
- science
A Python Pandas implementation of technical analysis indicators
Predicting price trends in cryptomarkets using an lstm-RNN for the use of a trading bot
- btc
- lstm
- rnn
- bitcoin
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
Technical indicator library for Elixir with no dependencies.
- elixir
- phoenix
- technical-analysis
- indicator
Stock price prediction using a deterministic algorithm inspired by LSTM, focusing on pattern recognition in historical data.