Implements MixingTransformer with separate sequence and variate attention mechanisms, Rotary Positional Embedding (RoPE), per-head QK RMSNorm, and PerDimScale. This architecture is a novel pattern for multivariate time series that can be extracted and reused in other transformer-based forecasting models.

Provides RevIN (Reversible Instance Normalization) with running statistics, RMSNorm, and PerDimScale layers. These are self-contained, framework-agnostic normalization modules that can be reused in any time series or deep learning project requiring adaptive normalization.

Implements CPM (Cross-Patch Memory) iterative RevIN refinement, a technique to improve forecast accuracy by iteratively adjusting normalization statistics across patches. This is a non-obvious, reusable pattern for enhancing RevIN-based forecasting.

Provides BatchedInContextXRegLinear for handling dynamic numerical, dynamic categorical, and static categorical covariates in time series forecasting. Includes one-hot encoding, ridge regression, and per-input normalization. This library can be extracted and used in any forecasting pipeline requiring covariate support.

A production-ready CLI script that reads a CSV file with time series data, applies TimesFM forecasting with configurable parameters (context length, horizon, quantiles, frequency), and outputs results to CSV. Demonstrates a clean, reusable pattern for wrapping a model API into a command-line tool.