How Patterns Dictate Learnability in Sequential Data
This work addresses the challenge of assessing model adequacy and complexity in sequential data analysis, which is incremental by building on existing information-theoretic metrics.
The paper tackles the problem of model misspecification in autoregressive models for sequential data by introducing a predictive information framework that quantifies learnability limits based on temporal patterns. It demonstrates that optimal predictors cannot exceed the intrinsic information limit of the data, validated through experiments on synthetic datasets.
Sequential data - ranging from financial time series to natural language - has driven the growing adoption of autoregressive models. However, these algorithms rely on the presence of underlying patterns in the data, and their identification often depends heavily on human expertise. Misinterpreting these patterns can lead to model misspecification, resulting in increased generalization error and degraded performance. The recently proposed evolving pattern (EvoRate) metric addresses this by using the mutual information between the next data point and its past to guide regression order estimation and feature selection. Building on this idea, we introduce a general framework based on predictive information, defined as the mutual information between the past and the future, $I(X_{past}; X_{future})$. This quantity naturally defines an information-theoretic learning curve, which quantifies the amount of predictive information available as the observation window grows. Using this formalism, we show that the presence or absence of temporal patterns fundamentally constrains the learnability of sequential models: even an optimal predictor cannot outperform the intrinsic information limit imposed by the data. We validate our framework through experiments on synthetic data, demonstrating its ability to assess model adequacy, quantify the inherent complexity of a dataset, and reveal interpretable structure in sequential data.