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timesfm-forecasting

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Zero-shot time series forecasting with Google's TimesFM foundation model. Use this skill when forecasting ANY univariate time series — sales, sensor readings, stock prices, energy demand, patient vitals, weather, or scientific measurements — without training a custom model. Automatically checks system RAM/GPU before loading the model, supports CSV/DataFrame/array inputs, and returns point forecasts with calibrated prediction intervals. Includes a preflight system checker script that MUST be run before first use to verify the machine can load the model. For classical statistical time series models (ARIMA, SARIMAX, VAR) use statsmodels; for time series classification/clustering use aeon.

bundled/skills/timesfm-forecasting/SKILL.md

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# TimesFM Forecasting

## Routing Boundary

Use this skill only for TimesFM, zero-shot forecasting, foundation forecasting, forecast horizons, prediction intervals, or TimesFM-specific time-series pipelines. Do not use it for generic business forecasting, ARIMA baselines, tabular regression, ordinary scikit-learn modeling, or exploratory time-series analysis without TimesFM/foundation-model signals.

## Overview

TimesFM (Time Series Foundation Model) is a pretrained decoder-only foundation model
developed by Google Research for time-series forecasting. It works **zero-shot** — feed it
any univariate time series and it returns point forecasts with calibrated quantile
prediction intervals, no training required.

This skill wraps TimesFM for safe, agent-friendly local inference. It includes a
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