arXiv Machine Learning

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.

arXiv Machine Learning

Sep 25

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.

By Stefano Damato, Lorenzo Zambon, Giorgio Corani, Dario Azzimonti

benchmarks

arXiv Machine Learning

Sep 15

Are Gradient Boosting Models Suitable for Intermittent Demand Forecasting?

arXiv:2609.14718v1 Announce Type: new Abstract: Demand forecasting is critical in modern industry, offering opportunities to reduce costs and gain competitive advantage through improved inventory man...

By Vladislav Kislinskii, Mazhar Hameed

arXiv Machine Learning

Jun 18

RNN(p) for Power Consumption Forecasting

arXiv:2209. 01378v3 Announce Type: replace Abstract: An elementary Recurrent Neural Network that operates on p time lags, called an RNN(p), is the natural generalisation of a linear autoregressive model ARX(p).

By Roberto Baviera, Pietro Manzoni

safety

arXiv Machine Learning

Sep 15

Accuracy Is Not Service: A Decision-Aware Benchmark for Intermittent-Demand Forecasting

arXiv:2609.13840v1 Announce Type: new Abstract: A contract-logistics spare-parts operator is paid on order-level service: an order counts only if every requested line is fulfilled, yet forecasters ar...

By Joo Ern Chin, Shih-Fen Cheng, Aldy Gunawan

benchmarks safety

arXiv Machine Learning

Jun 3

FinStressTS: A Parametric Synthetic Benchmark for Time-Series Forecasting in Finance

arXiv:2606. 03184v1 Announce Type: cross Abstract: Financial forecasting is difficult due to low signal-to-noise ratios, latent factors, heavy tails, regime shifts, and jumps.

By Jiaze Sun, Kelvin J. L. Koa, Ruiyang Ni, Yize Liu, Haonan Chen, Ke-Wei Huang

llms multimodal benchmarks safety

arXiv Machine Learning

Aug 4

AROpt: An Optimization Method for Autoregressive Time Series Forecasting

arXiv:2602. 02288v3 Announce Type: replace Abstract: Current time-series forecasting models are primarily based on transformer-style neural networks.

By Zheng Li, Jerry Cheng, Huanying Gu

llms benchmarks

arXiv AI

3d ago

Conformal Adversarial Generative Ensemble

arXiv:2609.38196v1 Announce Type: cross Abstract: Accurate time series forecasting is critical across various domains, yet traditional ensemble methods often suffer from the disproportionate influenc...

By Ahmad Shahi, Mamehgol Yousefi, Brendon J. Woodford, Farhaan Mirza, Tapabrata Chakraborti

safety

arXiv Machine Learning

Jul 21

Time-Aware Prior Fitted Networks for Zero-Shot Forecasting with Exogenous Variables

arXiv:2603. 15802v2 Announce Type: replace Abstract: In many time series forecasting settings, the target time series is accompanied by exogenous covariates, such as promotions and prices in retail demand; temperature in energy load; calendar and holiday indicators for traffic or sales; and grid load or fuel costs in electricity pricing.

By Andres Potapczynski, Ravi Kiran Selvam, Tatiana Konstantinova, Malcolm Wolff, Kin G. Olivares, Ruijun Ma, Michael W. Mahoney, Andrew Gordon Wilson, Boris N. Oreshkin, Dmitry Efimov

benchmarks

arXiv Machine Learning

Jun 9

Vessel Traffic Flow Prediction on Sparse Data via Spatio-Temporal Graph Neural Networks with a Learnable Tweedie Head

arXiv:2606. 07694v1 Announce Type: new Abstract: Accurate vessel traffic flow prediction is crucial for smart port operations and navigational safety.

By Kyeongjun Lee, Heeyoung Kim

safety

arXiv Machine Learning

Jul 14

Probabilistic Wind Power Forecasting with Tree-Based Machine Learning and Weather Ensembles

arXiv:2602. 13010v2 Announce Type: replace Abstract: Accurate production forecasts are essential for the integration of renewable energy sources into the power grid.

By Max Bruninx, Diederik van Binsbergen, Timothy Verstraeten, Ann Now\'e, Jan Helsen

diffusion benchmarks

arXiv Machine Learning

Jul 13

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility

arXiv:2510. 04487v5 Announce Type: replace Abstract: While accuracy is a critical requirement for time series forecasting, an equally important desideratum is reasonable forecast volatility across forecast creation dates (FCDs).

By Willa Potosnak, Malcolm Wolff, Mengfei Cao, Ruijun Ma, Tatiana Konstantinova, Dmitry Efimov, Michael W. Mahoney, Boris Oreshkin, Kin G. Olivares

llms benchmarks

arXiv Machine Learning

Sep 22

Time Series Foundation Models for Process Model Forecasting

arXiv:2512.07624v2 Announce Type: replace Abstract: Process Model Forecasting (PMF) aims to predict how the control-flow structure of a process evolves over time by modeling the temporal dynamics of...

By Yongbo Yu, Jari Peeperkorn, Johannes De Smedt, Jochen De Weerdt

fine-tuning efficiency benchmarks

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