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.

Wasserstein Exponential Smoothing for Distributional Time Series Forecasting

The paper introduces Wasserstein Exponential Smoothing (WES), a single‑parameter recursive method for forecasting distributional time series on ℝ. WES updates forecast distributions along Wasserstein geodesics, allowing direct application to empirical distributions without parametric modeling. In high‑frequency equity‑index return and household electricity‑demand data, WES achieves the lowest one‑step‑ahead Wasserstein prediction error among existing benchmarks and is retained in the 90% model confidence set for all 20 series examined.

FLAME: Flow Enhanced Legendre Memory Models for General Time Series Forecasting

FLAME is a lightweight Time Series Foundation Model that uses Legendre Memory variants (LegT and LegS) in its encoding and decoding stages to capture inductive biases and perform efficient long‑range forecasting. It incorporates a normalizing‑flow forecasting head to generate complex probabilistic distributions over future horizons. Experiments on TSFM‑Bench, ProbTS, and TFB show FLAME performs strongly as an out‑of‑the‑box tool for decision intelligence.

Probabilistic Low-Voltage Peak Load Forecasting with Time Series Foundation Models Evaluated on Application-Oriented Metrics

Low-voltage load forecasting is an important component in current and future energy systems with a high degree of electrification and decentralized generation. However, current forecasting methods require significant manual effort, often lack uncertainty estimation and proper peak prediction, and they are often not adequately evaluated in terms of grid requirements.

Probabilistic Low-Voltage Peak Load Forecasting with Time Series Foundation Models Evaluated on Application-Oriented Metrics

arXiv:2607. 01966v1 Announce Type: new Abstract: Low-voltage load forecasting is an important component in current and future energy systems with a high degree of electrification and decentralized generation.

Two-stage Odd Residual Flows for Mean-Preserving Probabilistic Time Series Forecasting

arXiv:2608. 11114v1 Announce Type: cross Abstract: Probabilistic forecasting plays an essential role in risk-sensitive decision-making, particularly in long-horizon settings.

Intermittent time series forecasting: local vs global models

arXiv:2601. 14031v2 Announce Type: replace-cross Abstract: Forecasting intermittent time series, which contain zeros, is a crucial challenge in supply chains as inventory policies require probabilistic forecasts to establish safety levels.