timesfm-forecasting
Zero-shot time series forecasting with Google's TimesFM foundation model. Use for any univariate time series (sales, sensors, energy, vitals, weather) without training a custom model. Supports CSV/DataFrame/array inputs with point forecasts and prediction intervals. Includes a preflight system checker script to verify RAM/GPU before first use.
npx skills add K-Dense-AI/scientific-agent-skills --skill timesfm-forecasting --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 wraps TimesFM for safe, agent-friendly local inference. It 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 a 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-fore
- Overview
- When to Use This Skill
- ⚠️ Mandatory Preflight: System Requirements Check
- Hardware Requirements by Model Version
- 🔧 Installation
- Step 1: Verify System (always first)
- Step 2: Install TimesFM
- Step 3: Install PyTorch for Your Hardware
- Step 4: Verify Installation
- 🎯 Quick Start
- Minimal Example (5 Lines)
- Forecast from CSV
- Forecast with Covariates (XReg)
- Anomaly Detection (via Quantile Intervals)
python scripts/check_system.py Using uv (recommended by this repo) uv pip install timesfm[torch] For JAX/Flax backend (faster on TPU/GPU) uv pip install timesfm[flax] CUDA 12.1 (NVIDIA GPU) uv pip install torch>=2.0.0 --index-url https://download.pytorch.org/whl/cu121 CPU only uv pip install torch>=2.0.0 --index-url https://download.pytorch.org/whl/cpu Apple Silicon (MPS)
What does the timesfm-forecasting skill do?
Zero-shot time series forecasting with Google's TimesFM foundation model. Use for any univariate time series (sales, sensors, energy, vitals, weather) without training a custom model. Supports CSV/DataFrame/array inputs with point forecasts and prediction intervals. Includes a preflight system checker script to verify RAM/GPU before first use.
How do I install it?
Run `npx skills add K-Dense-AI/scientific-agent-skills --skill timesfm-forecasting --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From K-Dense-AI/scientific-agent-skills, a repository with 32,619 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.