time-series
Time series analysis and forecasting techniques
Learn to upsample time-series gaps in Polars Rust to exact 5-minute intervals using date_range, vstack, and forward fill. Preserve non-aligned timestamps like 00:05:17 without replacement. Rust code examples for sensors data.
Fix PyTorch Dataset and DataLoader for multivariate time series preprocessing from CSV. Ensure (B, V, L) shapes, avoid data leakage with proper scaling, and validate sliding windows for MAMBA models.
Forecast stepwise price time series with change point detection, regime-switching (HMM), and survival models. Includes pipeline, libraries, and evaluation tips.
Is training a time series regression model on snapshots closer to departure data leakage when predicting earlier? Learn validation strategies, feature rules, and pitfalls to avoid lookahead bias in forecasting final bookings.