A Simple and Universal Swarm Intelligence Engine, Predicting Anything. ็ฎๆด้็จ็็พคไฝๆบ่ฝๅผๆ๏ผ้ขๆตไธ็ฉ
TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
Statsmodels: statistical modeling and econometrics in Python
Fast and Accurate ML in 3 Lines of Code
A unified framework for machine learning with time series
A python library for user-friendly forecasting and anomaly detection on time series.
Kats, a kit to analyze time series data, a lightweight, easy-to-use, generalizable, and extendable framework to perform time series analysis, from understanding the key statistics and characteristics, detecting change points and anomalies, to forecasting future trends.
Chronos: Pretrained Models for Time Series Forecasting
Probabilistic time series modeling in Python
Time series forecasting with PyTorch
Lightning โก๏ธ fast forecasting with statistical and econometric models.
NeuralProphet: A simple forecasting package
Scalable and user friendly neural :brain: forecasting algorithms.
The machine learning toolkit for time series analysis in Python
AssetOpsBench - Industry 4.0: A unified benchmark and framework for building, orchestrating, and evaluating domain-specific AI agents for Industry 4.0 asset operations and maintenance, with 460+ scenarios, 5 specialist agents (IoT, FMSR, TSFM, Work Order,...), and multi-agent orchestration blueprints (MetaAgent, AgentHive) over MCP.
Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).
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
A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function.
This repository features an Energy Optimization System (EOS) that optimizes energy distribution, usage for batteries, heat pumps& household devices. It includes predictive models for electricity prices (planned), load forecasting& dynamic optimization to maximize energy efficiency & minimize costs. Founder Dr. Andreas Schmitz (YouTube @akkudoktor)
ARCH models in Python
Python library for time series forecasting using scikit-learn compatible models, statistical methods, and foundation models
Simulate anything, for $1 & less than 10 min - Universal Swarm Intelligence Engine
A toolkit for time series machine learning and deep learning
Automated Time Series Forecasting
Django Ledger is a double entry accounting system and financial analysis engine built on the Django Web Framework.
Scalable machine ๐ค learning for time series forecasting.
Time Series Feature Extraction Library (TSFEL): 60+ statistical, temporal, spectral and fractal features for time series classification, regression and signal processing in Python
Implementation of the Aurora model for Earth system forecasting
Resources about time series forecasting and deep learning.
Probabilistic Hierarchical forecasting ๐ with statistical and econometric methods.
Training-free Spectrum acceleration for ComfyUIโs native MiniMax H3 audio-video model. Uses Chebyshev ridge feature forecasting to skip selected H3 transformer evaluations, with adaptive scheduling, sampler-aware support for Euler, ER-SDE, RES, SEEDS and SA-Solver, CPU/VRAM history storage, and fail-closed native fallbacks.
Predicting stock prices using a TensorFlow LSTM (long short-term memory) neural network for times series forecasting
A simple and flexible code for Reservoir Computing architectures like Echo State Networks
TimeCopilot: the GenAI Forecasting Agent. Built on LLMs and Time Series Foundation Models, it lets you forecast, cross-validate, and detect anomalies using multiple foundation models through a single API. From finance and energy to web analytics, TimeCopilot turns natural-language queries into production-ready forecasts.
A collection of diagnostic and interpolation routines for use with output from the Weather Research and Forecasting (WRF-ARW) Model.
Paper trading simulator for Polymarket
Official research code for GlucoFlow: few-step multimodal probabilistic glucose forecasting.
[Public alpha] Probability forecasting protocols, delayed-reward agent rollouts, and verifiable real-world task environments.
A map of modern time-series forecasting architectures, each with a short README and runnable code.
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A creator-first AI skill for topic generation, script review, filming guidance, and view forecasting.
Yug: a foundation model for zero-shot probabilistic time-series forecasting
Reproducible monthly demand forecasting with Random Forest, chronological backtests, transparent baselines, and a guided local app.
KiT: A Foundation Model for Financial Time-Series Forecasting using Diffusion Transformers
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A benchmark for evaluating LLMs on probabilistic social-event forecasting.
Kronos AI: Tiny Model That Beats Expensive Trading Bots - Open-source foundation model for financial candlesticks with Ollama Llama 3.2 Indian market analysis.
Cutoff-aware driver ratings and four-pool Formula 1 qualifying predictions (Q2, Q3, Top 3, pole)
Autonomous AI trading system powered by Kronos. Forecasts markets, builds portfolios and paper-trades automatically with GitHub Actions.
A self-updating awesome list of prediction markets, their data coverage, and tools - regenerated from structured data