Transformer Models for Enhanced Time Series Forecasting

Music City Center 

Time series forecasting is essential for various real-world applications, often requiring domain expertise and extensive feature engineering, which can be time-consuming and knowledge-intensive. Deep learning offers a compelling alternative, enabling data-driven approaches to efficiently capture temporal dynamics. This talk introduces a new class of Transformer-based models for time series forecasting, leveraging attention mechanisms while integrating principles from classical time series methods to enhance their ability to learn complex patterns. These models are highly versatile, effectively handling both univariate and multivariate time series data. Empirical evaluations demonstrate significant improvements over conventional benchmarks, showcasing the practical effectiveness of these models.

Time Series Forecasting

Transformer Models

Deep Learning

Attention Mechanism

Temporal Dynamics

Univariate and Multivariate Forecasting 

Section on Statistical Learning and Data Science