Modern Deep Learning Methods for Time Series Analysis

Abstract

Deep learning methods for time series analysis have become prominent tools in state-of-the-art predictive modeling, with applications spanning finance, transportation, energy, healthcare, climate science, and numerous other domains. In this thesis, we explore modern deep learning techniques that address two key challenges common across many domains: handling variable-structure inputs and quantifying prediction uncertainty, with the goal of building models that are both robust and adaptable. We demonstrate these advances across two distinct domains: electromagnetic field (EMF) exposure time series forecasting in modern wireless networks and variable-length time series classification (VTSC) in healthcare. The first contribution introduces EMForecaster, a novel deep learning framework designed for accurate EMF exposure prediction. The architecture employs hierarchical patching to capture temporal patterns at multiple scales, complemented by reversible instance normalization and mixing operations for efficient feature extraction. To enhance reliability, EMForecaster incorporates conformal prediction mechanisms that provide principled uncertainty quantification, enabling trustworthy forecasts with guaranteed coverage rates. A new Trade-off Score metric is developed to balance prediction reliability against interval width. Empirical evaluations demonstrate EMForecaster's superior performance across diverse EMF datasets, with improvements of up to 53.97% over transformer architectures for point forecasts, while maintaining optimal balance between prediction interval coverage and width. The second contribution presents a Stochastic Sparse Sampling (SSS) framework for variable-length time series classification, a prevalent challenge in healthcare applications. SSS addresses the inherent variability in clinical time series by intelligently sampling fixed windows to compute local predictions, which are then aggregated and calibrated to form robust global classifications. This approach is validated on the task of seizure onset zone (SOZ) localization using intracranial electroencephalography (iEEG) recordings from the Epilepsy iEEG Multicenter Dataset. SSS demonstrates superior performance compared to state-of-the-art methods across most medical centers, particularly excelling in out-of-distribution scenarios with previously unseen data sources. Additionally, SSS provides valuable post-hoc insights by visualizing temporally averaged local predictions throughout the signal. Together, these methodologies advance the state-of-the-art in time series analysis through innovative deep learning techniques, uncertainty quantification, and interpretability methods, offering significant improvements for both forecasting and classification tasks in real-world wireless network and healthcare applications.