timesfm

Zero-shot forecasting, tabular classification, and regression via MCP — exposes Google TimesFM 2.5 and TabFM v1.0.0 to AI assistants. Just attach a CSV and describe what you want to predict.

From-scratch reimplementation of Google TimesFM (time-series foundation model) plus the full pretraining pipeline Google never open-sourced. Trained and honestly evaluated at 70M.

A stock prediction application that uses Google's TimesFM (Time Series Foundation Model) to forecast stock prices from Yahoo Finance, with FastAPI serving as the backend API.

MCP for TimesFM-3: univariate or joint multivariate forecasts with q10–q90 and optional covariates. Weights are non-commercial.

Google TimesFM 3.0: This Tiny 330M AI Can Predicts Stocks Locally! (CPU Setup) - Predict stock market prices and market trends using Google's TimesFM 3.0 foundation model locally on CPU.

Zero-shot multivariate power grid load, market price and renewable energy forecasting for the Polish Power System (PSE) using Google TimesFM and weather covariates.

Tempolith: free, local-first time-series forecasting workbench. TimesFM and LightGBM side by side, backtests, diagnostics, anomaly detection, and what-if scenarios. Runs entirely on your machine.

Production-grade statistical arbitrage terminal using Google's TimesFM 2.5 and Kalman Filters for dynamic hedge ratio adaptation. Features a high-contrast Bloomberg-style dashboard, vectorized backtesting, and real-time news sentiment analysis.

Advanced Hybrid AI expert system for NASDAQ & Oil (WTI) ETF trading. Merges Quantitative ML, LLMs (Gemma 4, Gemini free or not), TimesFM 3, Visual Chart Analysis, and EIA Fundamentals for high-accuracy signals. Features dual-ticker strategy and Trading 212 execution.

Methodology lab for OjoAlTicker · Monte Carlo simulation · TimesFM forecasting · SLSQP optimization

Google's New TimesFM 2.5 time-series forecasting engine with automated financial market reports via Ollama, Gemma, and interactive Streamlit UI.

Pure MLX inference for Google TimesFM 3.0 on Apple silicon.

Causal TimesFM Engine v2.0: Institutional Time-Series Forecasting & Capital Allocation Engine combining Google TimesFM with Post-Keynesian Econometrics, 24/7 On-Chain Money Velocity, and Time-Varying Markov Regime Switching.

Polish election poll aggregator: a state-space model (additive log-ratio + Kalman filter) with pollster house effects estimated from data and calibrated uncertainty. pulswyborczy.pl

Experimental Python quantitative-research framework for multi-signal market analysis, historical backtesting, and ML-assisted forecasting.

Self-hosted forecasting + prediction service. Five zero-shot time-series foundation models (Chronos-2, TimesFM 2.5, Moirai-2, Toto-1, Sundial) across six forecast types, plus nine supervised tabular ML backends (LightGBM, XGBoost, sklearn family) with calibrated / stacking / diversified meta-learners. Unified REST API + MCP server.

Zero-shot TSFM forecasts (Chronos-2, TimesFM) meet constrained Markowitz optimization for dynamic equity portfolios. ー S&P 500 forecasting & portfolio optimization.

Cadence: Error-Bounded Lossy Compression of Demand Time Series with a Time-Series Foundation Model (Google TimesFM 3)

Institutional MetaTrader 5 algorithmic trading framework adapting FinRL via a 5-Expert Mixture-of-Experts (MoE) Council with NSGA-III Pareto gating on 100% real ticks.

Time-Series-Forecast-Transformer working on the local container.