Probabilistic Time Series Forecasting with 🤗 Transformers

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

Multivariate Probabilistic Time Series Forecasting with Informer

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.

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.

fable.intermittent: benchmarking probabilistic forecasting methods for intermittent time series

The paper introduces fable.intermittent, an R package that consolidates various probabilistic forecasting methods for intermittent time series within the fable framework, enabling streamlined fitting and evaluation across multiple datasets. It also presents TWEES, a new exponential smoothing model using a Tweedie predictive distribution, and releases tweedieDistr, a faster implementation of the Tweedie distribution. The authors evaluate these tools on four datasets provided with the package.