arXiv:2609. 20193v1 Announce Type: new Abstract: Retrieval plug-ins supply a deep forecaster with information its lookback window cannot carry.
By Mert Onur Cakiroglu, Elham Buxton, Mehmet Dalkilic, Hasan Kurban
The paper demonstrates that the outcome of a forecasting leaderboard is largely determined by the evaluator’s design choices rather than the models themselves. By fixing the data, horizon, and period, the authors varied three key evaluation decisions—unit of analysis, error pooling, and scoring metric—and showed that each can reverse or eliminate the apparent superiority of any forecasting method. The study also evaluates the practical impact of these choices on a deployed system, revealing that the selection rule captures a significant portion of the potential performance gain, and confirms the findings on an external public dataset.
By Md Rezwanul Islam, Wael Mohammed
arXiv:2502. 18834v3 Announce Type: replace-cross Abstract: Financial time series (FinTS) record the behavior of human-brain-augmented decision-making, capturing valuable historical information that can be leveraged for profitable investment strategies.
By Yifan Hu, Yuante Li, Peiyuan Liu, Yuxia Zhu, Naiqi Li, Tao Dai, Shu-tao Xia, Dawei Cheng, Changjun Jiang
arXiv:2608. 03259v1 Announce Type: cross Abstract: As time-series foundation models have emerged, the need for benchmarks that can evaluate their forecasting ability in meaningful ways has become increasingly important.
By Jaehoon Lee, Jun Seo, Seunghan Lee, Tae Yoon Lim, Dongwan Kang, Hwanil Choi, Minjae Kim, Sungdong Yoo, Junhyeok Kang, Sangjun Han, Soonyoung Lee, Wonbin Ahn
UQ-LOB is a lightweight, encoder‑agnostic module that adds uncertainty quantification to any pretrained limit order book (LOB) encoder. It offers two variants: UQ‑regression, which outputs a calibrated Gaussian over future tick displacement, and UQ‑classification, which outputs a categorical distribution over down/up/stationary. On 5.2 billion LOB events across seven cryptocurrency assets, UQ‑regression achieves near‑nominal 68 % interval coverage, and selecting the top 10 % most confident predictions boosts directional macro F1 by 0.11–0.15 for regression and 0.05–0.11 for classification, reaching F1 scores of 0.88 (down) and 0.83 (up) at a 5‑second horizon.
By Derrick Gilchrist Edward Manoharan, Eljas Linna, Kestutis Baltakys, Hao Dong, Juho Kanniainen
arXiv:2606. 28670v1 Announce Type: cross Abstract: We introduce MACROCAST, a lightweight Time Series Foundation Model (TSFM) for real-time macroeconomic forecasting.
By Andrea Carriero, Davide Pettenuzzo, Shubhranshu Shekhar