timeseriesforecasting

Modeling multi-asset time series requires more than just stacking variables sequentially. 📊

Understanding dependencies across both the time axis and the variable axis is key to building predictive systems.

Here is a structured approach for implementing 2D State-Space Models:

1. Treat multivariate time series as a two-dimensional problem, recognizing dependencies along both time and variable axes. 🗺️
2. Structure input variables carefully by grouping features semantically (e.g., microstructure, portfolio state, cross-market context).
3. Manage data leakage by building an availability matrix that records when each data point could first be observed. ⚠️
4. Benchmark performance against a comprehensive suite: last-value/seasonal-naive, linear autoregression, independent models, and attention mechanisms.
5. For deployment, treat the S-S Model as a service. Version the variable schema, context length, normalization state, and model weights together.
6. Efficiency is critical. Record training throughput, peak memory, and inference latency at the actual batch size. ⚙️

For a deeper dive into the mechanics of this architecture, review this resource: https://community.wolfbot.io/academy/state-space-models-multivariate-market-data

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