Time Series Forecaster

Overview

Specialized forecasting pipelines for time-dependent data. Handles trend analysis, seasonality detection, and future predictions using statistical methods, machine learning, and deep learning approaches—all integrated with SpecWeave's increment workflow.

Why Time Series is Different

Standard ML assumptions violated:

• ❌ Data is NOT independent (temporal correlation)
• ❌ Data is NOT identically distributed (trends, seasonality)
• ❌ Random train/test split is WRONG (breaks temporal order)

Time series requirements:

• ✅ Temporal order preserved
• ✅ No data leakage from future
• ✅ Stationarity checks
• ✅ Autocorrelation analysis
• ✅ Seasonality decomposition