# TimesFM Forecasting

Forecasts univariate time series zero-shot with Google's TimesFM, with optional covariates.

- Repository: https://github.com/google-research/timesfm (34993 stars)
- Category: Data and ML
- License: Apache-2.0
- Last updated: 2026-09-29
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add google-research/timesfm --skill timesfm-forecasting -g -a claude-code -y`
Codex: `npx skills add google-research/timesfm --skill timesfm-forecasting -g -a codex -y`
Cursor: `npx skills add google-research/timesfm --skill timesfm-forecasting -g -a cursor -y`
Gemini CLI: `npx skills add google-research/timesfm --skill timesfm-forecasting -g -a gemini-cli -y`

## Description

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. Supports both basic forecasting and advanced covariate forecasting (XReg) with dynamic and static exogenous variables. Automatically checks system RAM/GPU before loading the model, validates dataset fit before processing, 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 and handle your specific dataset.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# TimesFM Forecasting

## 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 includes a **mandatory preflight system checker** that verifies RAM, GPU memory,
and disk space before the model is ever loaded so the agent never crashes the user's machine.

> **Key numbers**: TimesFM 2.5 uses 200M parameters (~800 MB on disk, ~1.5 GB in RAM on
> CPU, ~1 GB VRAM on GPU). The archived v1/v2 500M-parameter model needs ~32 GB RAM.
> Always run the system checker first.

## When to Use This Skill

Use this skill when:

- Forecasting **any univariate time series** (sales, demand, sensor, vitals, price, weather)
- You need **zero-shot forecasting** without training a custom model
- You want **probabilistic forecasts** with calibrated prediction intervals (quantiles)
- You have time series of **any length** (the model handles 1, 16,384 context points)
- You need to **batch-forecast** hundreds or thousands of series efficiently
- You want a **foundation model** approach instead of hand-tuning ARIMA/ETS parameters
- You need **covariate forecasting** with exogenous variables (price, promotions, holidays, day-of-week effects) → use `forecast_with_covariates()` (TimesFM 2.5 + `pip install timesfm[xreg]`)


Do **not** use this skill when:

- You need classical statistical models with coefficient interpretation → use `statsmodels`
- You need time series classification or clustering → use `aeon`
- You need multivariate vector autoregression or Granger causality → use `statsmodels`
- Your data is tabular (not temporal) → use `scikit-learn`
- You cannot install optional dependencies → XReg requires scikit-learn and JAX

The rest: https://github.com/google-research/timesfm/blob/HEAD/timesfm-forecasting/SKILL.md