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arXiv Machine Learning

CEDAR: Controlled and Event-Driven Demand Forecasting via Residual Decomposition

CEDAR is a two‑stage framework for demand forecasting that incorporates planned actions and external event signals. Stage I uses an Action‑Interleaved Transformer to model controllable state transitions under interventions, while Stage II applies a Residual Correction Module that aligns event descriptions with product context using LLM‑assisted text representations. Experiments on a large Alibaba 1688 dataset show that CEDAR improves simulation accuracy over traditional time‑series forecasting baselines and benefits real‑world budget planning.

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arXiv Machine Learning
Oct 28

TimeWorld: Towards Action-Conditioned Multimodal World Models for Time Series Forecasting

TimeWorld introduces a plug‑in framework for action‑conditioned multimodal world models in time‑series forecasting. It builds on pretrained time‑series foundation models (TSFMs) and adds a Prototype‑Guided Latent Modulation module to embed textual actions into temporal dynamics, along with an Action Contrastive Learning component to sharpen action‑specific response modeling. The authors also present TSWM‑Bench, a cross‑domain benchmark of seven real‑world datasets with textual interventions, showing that simple text‑prefix adaptations outperform conventional multimodal forecasters and that pretrained TSFMs already encode useful world knowledge for action‑conditioned predictions.

By Hang Ni, Weijia Zhang, Hao Liu
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arXiv AI
Sep 16

Repurposing Deep Limit Order Book Forecasting for Scenario-Conditioned Market Impact Modeling

The paper demonstrates that deep limit order book forecasting models can be repurposed to quantify scenario-conditioned market impact without retraining. By injecting counterfactual order‑book messages into a trained Transformer forecaster, the authors compare predictive distributions before and after the injection, defining a short‑horizon model‑implied market impact. The approach achieves a Spearman correlation of 0.99 and 97.2% directional agreement with historical outcomes for non‑neutral scenarios, and captures incremental sequence‑dependent variation beyond scenario identity and pre‑event forecasts.

By Eljas Linna, Kestutis Baltakys, Derrick Manoharan, Alexandros Iosifidis, Juho Kanniainen
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arXiv Machine Learning
Jun 10

One Step Closer to Ground Truth: A Multi-Scale Residual-Aware Representation Learning Pipeline for Predicting Time Series Data

arXiv:2606. 10678v1 Announce Type: new Abstract: Transformer-based models have emerged as leading paradigms in time-series forecasting in recent years, employing self-attention mechanisms to capture long-range dependencies.

By Amrijit Biswas, Mustafa Kamal, Robin Krambroeckers, M. M. Lutfe Elahi, Sifat Momen, Nabeel Mohammed, Shafin Rahman
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arXiv AI
Oct 2

On the Divergence of Accuracy and Mechanism Consistency in Time Series World Models

The paper introduces a formal framework and benchmark for time‑series world models (TSWMs) that separates state, actions, and exogenous inputs, and defines a new metric called mechanism consistency to evaluate whether model predictions move in the expected direction when actions change. Experiments on eight public datasets show that using a frozen latent prediction space and gated output fusion improves prediction accuracy, while prediction error and mechanism consistency often diverge, with the best‑performing models sometimes failing to exhibit consistent directional responses. Adding a directional supervision loss significantly boosts mechanism consistency without affecting mean‑absolute error, providing a practical recipe for building more reliable TSWMs.

By Haochen Zhang, Jiaheng Guo, Zhen Xu, Zachary Plotkin, Nicholas Konz, Zhen Tan, Tianlong Chen
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