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arXiv Machine Learning

On the Encoding of Calendar Features in Covariate-Informed Time Series Foundation Models

The paper investigates how different calendar feature encodings influence the performance of covariate-informed time series foundation models (TSFMs). Seven encoding schemes—three target‑agnostic (ordinal, one‑hot, cyclic) and four target‑based—are systematically compared across four state‑of‑the‑art TSFMs on nine datasets that reflect social activity patterns such as weekends and holidays. Results show that encoding choice can change forecast accuracy by up to 9.9 % on non‑holidays and 83 % on holidays, with improvements mainly around holidays and varying effects across models.

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arXiv Machine Learning
Oct 3

Foundations without Fundamentals: Zero-Shot Blind Spots in Time Series FMs

The paper introduces SimpleTimeBench, a diagnostic suite testing basic temporal primitives like monotonic trends, periodic signals, and leading indicator covariates. It finds that prominent multivariate Time Series Foundation Models (Chronos‑2, Moirai, and Toto) often produce suboptimal zero‑shot forecasts for these simple patterns, and that fine‑tuning can improve specific tasks while harming performance on other fundamentals. These failures persist in real‑world sensor forecasting, indicating that current TSFMs may lack the inductive biases needed to capture straightforward relationships, thereby limiting their practical reliability.

By Nafiseh Ghoroghchian, Haipeng Zhang, Shuyi Han, Alex Labach, George Stein
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arXiv Machine Learning
Oct 23

WhatIf-Bench: Benchmarking Intervention-Aware Time Series Forecasting

WhatIf-Bench is a semi‑synthetic benchmark that tests how well time‑series forecasters use covariates to predict a target’s response to future events. It generates 37,665 tasks from mechanistic simulators of data centers, power grids, road traffic, and water networks, varying covariate numbers, historical events, and event length. Evaluation shows large‑language models outperform the best time‑series foundation models, with all models performing well on replayed events but struggling on novel covariate settings, highlighting generalization as the main challenge.

By Arjun Ashok, Mihir Parmar, Palash Goyal, Bhavana Dalvi Mishra, Chun-Liang Li, Irina Rish, Alexandre Drouin, Jinsung Yoon, Tomas Pfister
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arXiv AI
Oct 12

Machine Learning and Time-Series Foundation Models for Day-Ahead Electricity Forecasting in a University Building

The study benchmarks various forecasting models—including statistical, machine‑learning, recurrent neural networks, and pretrained TimesFM—for day‑ahead electricity demand in a university building. TimesFM 3.0 with weather covariates achieves the lowest overall mean absolute error, outperforming a weekly seasonal‑naive baseline by about 43%, while XGBoost remains competitive and requires only local fitting. The LSTM model takes longer to train without improving accuracy, and TimesFM’s performance is robust even without covariates, though covariates slightly improve holiday forecasts.

By Amey Vasulkar, Samuel Kernan Freire, Arie-Willem de Leeuw, Tadeo-Baldiri Salcedo-Rahola
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arXiv Machine Learning
Oct 9

RideBench: A Large-Scale Exogenous-Aware Benchmark for Ride-Hailing Time Series Forecasting

RideBench is a new benchmark built on a large-scale ride‑hailing time‑series dataset called Ride‑Hailing, which spans four years of half‑hourly data across 200 spatial areas and includes three exogenous scenarios: weather disturbance, holiday effect, and large‑scale event impact. The benchmark evaluates over 30 forecasting methods for both week‑ahead and 8‑week‑ahead horizons, revealing that while future‑known exogenous variables improve week‑ahead accuracy, current models still struggle with disturbance‑induced pattern changes and long‑horizon forecasting. These results highlight a mismatch between existing models and real‑world ride‑hailing needs, underscoring the necessity for better exploitation of exogenous information, scalability, and long‑horizon planning capabilities.

By Shengsheng Lin, Jing Hu, Zhengyang Hu, Jiazheng Sun, Zichun Cao, Siwei Sun, Zhichao Zou, Enyun Yu, Dongdong Li, Xinyi Hu, Weiwei Lin
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arXiv Machine Learning
2d ago

Model-agnostic predictability quantification of multivariable time series

The paper introduces the Multivariable Predictability Score (MAPS), a model‑agnostic metric designed to quantify the intrinsic predictability of multivariable time‑series forecasting tasks. MAPS leverages covariate information across various lookback windows and forecasting horizons to assess predictive information, addressing limitations of existing measures that often miss nonlinear dependencies or focus on univariate series. Experiments on synthetic and real‑world datasets show MAPS aligns closely with expected reference predictability (r = 0.963) and provides more reliable projections than six baseline measures.

By Yuxuan Shu, Vasileios Lampos
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