timesfm-forecasting

Trigger phrases

Phrases that activate this skill when typed to Claude Code:

• forecast this time series
• predict future values
• TimesFM forecast
• zero-shot forecasting
• time series prediction

What it does

timesfm-forecasting is a Claude Code skill from K-Dense AI’s scientific-agent-skills repo. It turns Claude into a TimesFM deployment guide for zero-shot time series forecasting using Google’s TimesFM foundation model — no model training required. It accepts CSV files, pandas DataFrames, or NumPy arrays as input and produces point forecasts with prediction intervals for any univariate time series.

A session produces forecasts ready for plotting or downstream use, plus a preflight system check that verifies RAM and GPU availability before running TimesFM — since the foundation model has non-trivial hardware requirements.

The skill was authored by Clayton Young / Superior Byte Works, LLC and distributed through the K-Dense AI collection.

When to use it

Reach for it when:

• You need a quick, reasonable forecast on a new time series without the overhead of training an ARIMA, Prophet, or custom neural model
• You want to benchmark a custom model against a strong zero-shot baseline before investing in training
• You’re forecasting multiple time series of the same type (e.g., sensor readings across 100 devices) and want to run them through a single pre-trained model

When not to reach for it:

• Multivariate time series where cross-variable dependencies matter — TimesFM is univariate
• Time series where you have strong domain knowledge to incorporate through explicit model structure — statsmodels ARIMA or Prophet allows that

Install

Copy the SKILL.md from K-Dense AI’s timesfm-forecasting folder into .claude/skills/timesfm-forecasting/ in your project. Run the preflight checker script first to confirm your environment has sufficient RAM and GPU for TimesFM.

Trigger phrases: “forecast this time series”, “predict future values”, “TimesFM forecast”, “zero-shot forecasting”.

What a session looks like

A typical session has three phases:

1. Preflight check. Claude runs the included system checker script to verify RAM and GPU availability before loading the TimesFM model weights, which can be several GB.
2. Data ingestion. Provide the time series as a CSV file, DataFrame, or array with timestamps. Claude handles frequency detection and any required resampling.
3. Forecast generation. TimesFM runs inference and returns point forecasts and prediction intervals for the specified horizon. Results are plotted with the historical series and forecast overlaid.

Receipts

Where it works well:

• Quick baseline forecasts for business time series (sales, demand, energy consumption) where the zero-shot performance is competitive with traditional statistical methods without any tuning
• Batch forecasting across many similar series — the skill’s preflight and inference pipeline handles this cleanly

Where it backfires:

• Highly irregular or sparse time series where the foundation model’s training distribution doesn’t cover the pattern
• Environments with limited RAM or no GPU — the preflight checker will catch this, but TimesFM is not suitable for lightweight inference

Pattern that works: run the preflight check first every time, not just on first use — model weights and environment configurations change, and the checker saves frustration from mid-run failures.

Source and attribution

Skill authored by Clayton Young / Superior Byte Works, LLC (@borealBytes). The canonical SKILL.md lives in the timesfm-forecasting folder of K-Dense AI’s scientific-agent-skills repository.

License: Apache-2.0. Install, adapt, and redistribute with attribution preserved.

This page documents the skill from a practitioner’s perspective. For the formal spec and any updates, defer to the source repo.