Yes, Transformers are Effective for Time Series Forecasting (+ Autoformer)

Probabilistic Time Series Forecasting with 🤗 Transformers

A Benchmark for Electrical Load Forecasting Across Grid Levels: Time-Series Transformers Outperform Established Methods

arXiv:2607. 15705v1 Announce Type: new Abstract: Accurate load forecasting at multiple grid levels is essential for future smart grids, ranging from aggregated control area forecasts for balancing supply and demand to forecasts of individual end-consumer loads for demand-side management and energy management systems.

Fewer yet critical: Reducing Redundant Token Dependencies for Transformer-based Time Series Forecasting

The paper introduces a token dependency selection strategy for Transformer-based time series forecasting. By jointly applying an attention entropy constraint and a prediction error constraint, the method identifies fewer but more critical inter-token dependencies, reducing the influence of redundant dependencies that can hurt generalization. Experiments on multiple datasets show that this approach improves forecasting performance across various Transformer models.

Why Transformers Need Positional Encoding For Time Series: A Visual Guide

The article explains how transformers, which rely on self‑attention, can lose the natural order of time‑series data when fed scalar observations. It discusses the role of positional encoding in re‑introducing sequence order and provides a visual guide to illustrate this concept.

Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecasting

arXiv:2607. 22299v1 Announce Type: cross Abstract: Forecasting multiple time-series with high-dimensional covariates presents a core challenge: unifying common temporal patterns while retaining meaningful series-specific information.

Introducing Optimum: The Optimization Toolkit for Transformers at Scale