Forecasting Techniques and Applications

Neueste Paper

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

  Haochen Zhang, Jiaheng Guo, Zhen Xu, Zachary Plotkin, Nicholas Konz, Zhen Tan, Tianlong Chen · 2. Oktober 2026

  A time series world model (TSWM) predicts a controlled system's state from its observed history and planned actions and exogenous inputs. Current approaches build forecasters with actions as covariates, trained and evaluated on prediction error under the executed plan. Yet world models compare unexe…

• ProtoFlow: Prototype-Guided Flow Matching for Multivariate Time Series Forecasting

  Shibo Feng, Wanjin Feng, Yang Qiu, Deheng Ye, Peilin Zhao, Chunyan Miao · 2. Oktober 2026

  Generative modeling has shown strong promise for multivariate time mseries (MTS) forecasting, especially scale to high-dimensional settings. Diffusion-based methods achieve competitive performance but typically require many sampling steps at inference. VAE-based non-iterative forecasting frameworks …

• Pseudo-Label-Triggered Retraining from Forecast Errors for Online Time Series Forecasting

  Yeryeong Kwak, Yoo-Min Jung, Jonghun Park · 1. Oktober 2026

  Real-world time series forecasting systems operate under non-stationary data streams, where forecasting performance may degrade over time. Although retraining can recover the performance, it incurs non-trivial computational and operational costs. Under limited deployment resources, the key challenge…

• Conformal Adversarial Generative Ensemble

  Ahmad Shahi, Mamehgol Yousefi, Brendon J. Woodford, Farhaan Mirza, Tapabrata Chakraborti · 1. Oktober 2026

  Accurate time series forecasting is critical across various domains, yet traditional ensemble methods often suffer from the disproportionate influence of extreme forecasts. We introduce the Conformal Adversarial Generative Ensemble (CAGE), a novel framework that combines generative modeling, adversa…

• CAME: Company-Aware Evidence-Memory Experts for Interpretable Quarter-Ahead Revenue Forecasting

  Ya-Wen Wu, Meng-Fen Chiang, Kuang-Da Wang, Wen-Chih Peng · 30. September 2026

  Quarter-ahead revenue forecasting requires company-scale numerical accuracy, strict temporal validity, and company-specific interpretation of narrative disclosures. LLMs can distill textual evidence but can produce scale-misaligned forecasts, whereas history-based anchors are stable but miss forecas…

• Loss-Guided Pretraining Data Selection for Time-Series Foundation Models

  Yike Li, Shaoxu Song, Jianmin Wang · 30. September 2026

  Time series foundation models (TSFMs) are pretrained on heterogeneous collections containing billions of observations, yet their training windows are typically sampled without estimating whether they provide useful learning signal. We introduce a static data-selection framework that scores each wind…

• JudgeCast: Time Series Forecasting with Experience-Informed Covariate Judgements

  Donguk Kwon, Wooseok Jeong, Dongha Lee · 30. September 2026

  Covariate effects vary across contexts and shift over time, requiring forecasters to assess how to use them for each forecasting context. As forecasting proceeds, observations for earlier forecasts become available, providing feedback on past covariate use for subsequent forecasts. However, when mul…

• BITS: Rethinking Fair and Comprehensive Evaluation for Irregular Time Series Forecasting

  Kangjia Yan, Linfeng Wang, Tianen Shen, Xiangfei Qiu, Ruitong Zhang, Hao Miao, Jilin Hu, Chenjuan Guo, Bin Yang, Christian S. Jensen · 29. September 2026

  Despite recent progress in irregular time series forecasting, the field still lacks a unified benchmark for fair and comprehensive evaluation. Existing evaluations are often conducted on a limited set of datasets with inconsistent experimental protocols and predominantly error-based metrics, renderi…

• DiffPTS: Rethinking Diffusion ELBO for Probabilistic Time Series Forecasting

  Weiwei Ye, Dongyuan Li, Hangchen Liu, Haotong Jiang, Yoshihide Sekimoto, Renhe Jiang · 29. September 2026

  Probabilistic time series forecasting requires modeling and predicting complex and time-varying distributions. Recently, Denoising Diffusion Probabilistic Model (DDPM)-based approaches have shown promise by equipping the dif- fusion process with pretrained mean and variance estimators to accommodate…

• Dynamical Parameters: An Interpretability Framework for Time-Series Foundation Models

  Kang Yang, Gaofeng Dong, Liying Han, Mani Srivastava · 29. September 2026

  This work studies a central gap in interpreting time-series foundation models (TSFMs): a dynamical property may be accessible in a hidden state even when the forecast fails to respond correctly as that property changes. We formalize these properties as Dynamical Parameters, including trend slope, os…

• Can LLMs Predict the Future? A Brier Score Analysis of Prediction Markets

  Yuanbo Li, Zekun Li, Xiaoyan cong · 29. September 2026

  We study whether model upgrades improve probability estimates for prediction-market questions. Our Resolved Market Forecasting (RMF) benchmark contains 3,000 resolved binary questions across nine domains, on which we evaluate six Claude and Qwen model variants using a question-only, zero-shot protoc…

• Fracast-0: Fractal Weight Sharing for a Time Series Foundation Model with Only 85K Parameters

  Tianxiang Zhan, Huanyao Zhang, Yuanpeng He · 29. September 2026

  Time series foundation models must preserve multi-domain breadth, probabilistic output, and multiple temporal scales, but parameter count grows when each scale receives a separate representation. We introduce Fracast-0, a probabilistic forecasting foundation model that exploits temporal self-similar…

• Proper Scoring Rules for Right-Censored Survival Data

  Jef Jonkers, Glenn Van Wallendael, Luc Duchateau, Sofie Van Hoecke · 28. September 2026

  Proper scoring rules provide a rigorous theoretical basis for the training and evaluation of probabilistic forecasts. In survival analysis, such forecasts describe the distribution of the time until an event occurs. However, this event time is often only partially observed because follow-up may end …

• WorldTS: World Modeling for Multimodal Covariate-aware Time Series Forecasting

  Yuhan Zhu, Xiangfei Qiu, Hanyin Cheng, Wangmeng Shen, Chenjuan Guo, Bin Yang, Jilin Hu, Christian S. Jensen · 28. September 2026

  Time series forecasting is typically framed as learning a direct mapping from historical to future observations in the observation space. However, sequences of observations generally provide only a partial view of the dynamics of the underlying system, with future observations being shaped by latent…

• EPOC: Endpoint-Preserving Online Correction With Compressed Residual State for Multi-Horizon Time Series Forecasting

  Takumi Fujimoto, Hiroaki Nishi · 28. September 2026

  Completed multi-horizon forecasts provide residual feedback for a fixed forecaster, but retaining full residual blocks increases auxiliary state. We propose Endpoint-Preserving Online Correction (EPOC) with a compressed residual state. It stores low-order discrete cosine transform (DCT) coefficients…

• Seasonal and Quantum-inspired Models for Neutron Monitor Time Series Forecasting

  Krishna Bhatia, Shalini Devendrababu, Srinjoy Ganguly · 28. September 2026

  We present a focused and reproducible study of multi-horizon forecasting on the Lomnicky Stit neutron monitor (LMKS) time series. Our evaluation suite covers simple seasonal baselines, modern deep sequence models, and functional and quantum-inspired architectures, including Seasonal Naive, Long Shor…

• EXAONE Demand 1.0: A Time Series Foundation Model for Demand Forecasting

  Seunghan Lee, Sangjun Han, Jun Seo, Junhyeok Kang, Jaehoon Lee, Tae Yoon Lim, Dongwan Kang, Hwanil Choi, Minjae Kim, Sungdong Yoo, Soonyoung Lee, Wonbin Ahn · 28. September 2026

  Time series foundation models (TSFMs) are pretrained on series from diverse domains, where demand series make up only a small fraction. Demand data has properties that such corpora rarely contain: Short histories, frequent zeros, censoring by stock-outs, and exogenous events that the series does not…

• The Impossible Trinity of Time-Series Validation: A Conservation Law among Training Sufficiency, Test Coverage, and Temporal Causality

  Jiayu Li · 25. September 2026

  Validating a model on a time series asks for three things at once: each training run should use most of the sample (sufficiency), the test sets should together cover most of the sample (coverage), and training data should come before test data (causality). We prove that the three cannot be had toget…

• BridgeMem: Causal Dyadic Transition Residuals for Temporal Knowledge Graph Forecasting

  Zeyan Li, Libing Chen, Shengda Zhuo, Yin Tang, Jianfeng Xu · 25. September 2026

  Temporal knowledge graph forecasting aims to infer future relational facts from the temporal structure of observed events. Existing forecasters mainly summarize history through entity states, relation states, paths, or exact recurrence. These views often miss pair-specific transition evidence, that …

• Downside-Controlled Online Forecast Combination under Delayed and Revised Outcomes

  Minkyoung Kim, Hyunjung Byun, Yohan Lee, Beakcheol Jang · 25. September 2026

  Post-hoc correction adjusts a forecaster that cannot be retrained, such as a foundation model, but a correction fitted where errors are stable can hurt where they shift. We aim for downside control: not much worse than the starting forecast. We combine the frozen forecaster, a static corrector and a…

• fable.intermittent: benchmarking probabilistic forecasting methods for intermittent time series

  Stefano Damato, Lorenzo Zambon, Giorgio Corani, Dario Azzimonti · 25. September 2026

  Intermittent time series are common in spare-parts demand and retail sales. Since the cost of forecast errors is typically asymmetric, decisions such as inventory control require the full predictive distribution rather than a point forecast. Many probabilistic forecasting methods have been proposed;…

• Time-Series Foundation Models That Understand Data Revisions

  Taimoor Ahmad · 25. September 2026

  Historical observations are not always fixed: statistical agencies revise previously published values as new evidence arrives. Forecasting from a contemporary download can therefore expose a model to information unavailable at the date it purportedly made a prediction. We propose VINTAGE-TS, a revis…

• SGA: Uncertainty Quantification for Multi-Step Forecasting in Time Series Foundation Models

  Xin-Yu Hu, Shuang Liang, Cheng Feng, Shao-Qun Zhang · 25. September 2026

  The recent emergence of Time Series Foundation Models (TSFMs) has significantly advanced multi-step forecasting performance, enabling accurate predictions over extended future horizons. However, existing TSFMs often suffer from significantly inherent uncertainty, which typically manifests as derived…

• Forecast-Dojo: Replayable Environments for Benchmarking and Training LLM Forecasting Agents

  Liqin Ye, Haorui Wang, Fardin Ahmed, Rongzhi Zhang, Yuan He, Ziyuan Lin, Yanbin Yin, Jing Peng, Michael Galarnyk, Sudheer Chava, Chao Zhang · 25. September 2026

  We introduce Forecast-Dojo, a replayable environment for benchmarking and training LLM forecasting agents. It combines resolved prediction-market questions with dated news, allowing agents to research an event and revisit their predictions at successive historical dates. The same tasks and tools sup…

• TW3Cast: A Frozen Router of Lightly Fine-Tuned Foundation Models for Time-Series Forecasting on GIFT-Eval, Selected Entirely on the Training Split

  Nathan Thierry, Andre-Louis Rochet · 25. September 2026

  TW3Cast is a time-series forecasting system that reaches position 3 of 130 entries on the GIFT-Eval benchmark by mean MASE rank, as of 2026-09-14. The two entries above it belong to the leaderboard's agentic category, multi-step systems that use agents or language models to reason about, generate or…