Forecasting Techniques and Applications

Latest papers

• Conformal Prediction for Time Series with Deep Sequence Models

  Junghwan Lee, Jonghyeok Lee, Yao Xie · 5 October 2026

  Recent advances in deep learning for time series prediction have amplified the need for reliable uncertainty quantification. Conformal prediction has gained attention as a distribution-free framework for constructing prediction intervals with coverage guarantees. However, its coverage guarantees rel…

• Forecasting from Counterfactual Simulator Rollouts: A Sim2Real Evaluation

  Angel Wang, Dominique Perrault-Joncas, Alvaro Maggiar, Dean Foster, Carson Eisenach · 5 October 2026

  Deploying a new decision policy creates a cold-start problem for prediction models whose targets depend on the policy's actions: historical observations reflect earlier policies, while real observations under the new policy are not yet available. Simulation offers a way to address this gap by rollin…

• Most-Recent Anchoring with Recurrent Ordering for Time Series Forecasting

  Jung Min Choi, Ngoc Son Le, Ibram Abdelmalak, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme · 5 October 2026

  Long-term forecasting models commonly process all patches in a look-back window using the same fixed stack. Older contextual patches and recent evidence therefore receive the same computational depth. Yet the information closest to the forecast and the more distant context do not contribute equally.…

• Dual-Context Analog Retrieval for Time Series Forecasting

  Jung Min Choi, Ngoc Son Le, Ibram Abdelmalak, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme · 5 October 2026

  Most long-term time-series forecasting models map the look-back window directly to the full horizon in a single pass. While efficient, this design does not explicitly identify which historical states are most relevant to different future segments or exploit what followed those states. Analog forecas…

• Signal Simplification Is Not Predictive Simplification: Diagnosing Residual Neural Forecasting in Short-Horizon Volatility

  Bingqi Lian, Linfeng Cheng, Mei Lu, Jerry Wu · 5 October 2026

  Hybrid statistical-neural pipelines often assume that a successful statistical first stage leaves a cleaner and more learnable residual target. We examine that assumption in short-horizon volatility forecasting through a signal-forecast-system diagnostic framework. Across five liquid U.S. assets, a …

• MACTS-EM: Multi-Agent Collaborative Time Series Forecasting with Emergent Memory

  Ahmad Shahi, Mamehgol Yousefi · 5 October 2026

  Time series forecasting remains a critical challenge across numerous domains. Despite significant advancements, existing approaches struggle with complex phenomena such as regime shifts, cross-domain knowledge transfer, and multimodal data integration. This paper introduces Multi-Agent Collaborative…

• Time Series Forecasting Benchmarks Need Scenario-Grounded Stress Testing

  Yuyang Zhao, Lian Xu, Hao Xue · 5 October 2026

  Time series forecasting (TSF) increasingly drives decisions in transportation, energy, finance, healthcare, and infrastructure, yet current evaluation remains overly narrow: standard benchmarks reward low held-out error, while robustness studies typically reduce failure to Gaussian noise, random mas…

• 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 October 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 October 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 October 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 October 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…