What are the challenges and solutions for dealing with high-dimensional time series data?
Instruction: Discuss the specific challenges posed by high-dimensional time series analysis and the methodologies to address these challenges.
Updated
Example Answer
Many variables and lags can produce more parameters than the available history supports. I would worry about overfitting, redundant signals, unstable correlations, missingness, and the cost of fitting and serving the model.
I would compare a small predictive subset, regularized models, and a factor or PCA representation fitted only on the training period. For a fleet of sensors, the goal might be to retain shared signals while preserving important unit-specific failures. I would use t-SNE primarily for exploratory visualization, not assume its fitted embedding provides a standard transform for new forecast observations. I would test the full pipeline on later periods, including missing-input cases, and measure whether any dimension reduction discards information the decision needs.
Make it your own
Specify whether you have many variables in one system, many separate series, or both.
Why this works
It connects the high-dimensional problem to leakage-safe representation choices.
Follow-up to practice
How would you transform a new observation consistently?