概要
The Time Series Forecaster skill empowers Claude to design and implement advanced forecasting pipelines specifically tailored for temporal data. It moves beyond standard machine learning assumptions by enforcing rigorous best practices such as temporal train/test splitting, stationarity checks, and seasonality decomposition. Whether you need classic ARIMA models for statistical rigor, business-focused Prophet forecasts for holiday awareness, or deep learning-based LSTM sequences for complex non-linear patterns, this skill provides the necessary structure to predict future trends and quantify uncertainty. Every model is integrated into the SpecWeave increment workflow, ensuring that your forecasting logic is backed by automated tests, stationarity reports, and living documentation.
