Time Series Forecasting

Ask about this topic

Overview

Time series forecasting studies how to predict future observations from past values, often across multiple horizons and with missing data, changing regimes, or external drivers such as weather and events. Methods range from classical autoregressive and exponential-smoothing models to recurrent networks, Transformers, state-space models, decomposition and frequency-based approaches, graph models, and pretrained foundation models; probabilistic methods also estimate uncertainty. Current work increasingly combines historical patterns with covariates, retrieval, domain knowledge, and online adaptation, while targeting applications from energy and weather to health, transport, and finance. A central tension is that larger or more complex models do not reliably beat simple baselines, and benchmark accuracy may not reflect robustness, forecast structure, uncertainty calibration, or downstream decision value.

Latest papers

CardsList

875 papers143 papers in the last four weeks, up 32% on the four weeks before. 0.8% of all new papers.

  1. EF-LLM: A Continual Learning-Based Framework for AI-Assisted Automation, Enhanced Sparse Prediction, and Hallucination Detection

    Oct 30, 2024Zihang Qiu, Jayashri Ravishankar, Anam Malik +2LLM-Based Time Series ForecastingTime Series Forecasting

  2. Bellman Conformal Inference: Calibrating Prediction Intervals For Time Series

    Feb 7, 2024Zitong Yang, Yu Ming Huang, Emmanuel Candès +1Conformal PredictionProbabilistic Time Series Forecasting

    Department of Statistics, Stanford University · Stanford Graduate School of Bussiness

  3. FreDF: Learning to Forecast in the Frequency Domain

    Feb 4, 2024Hao Wang, Licheng Pan, Zhichao Chen +6Frequency-Domain Feature LearningTime Series Forecasting

    Department of Control Science and Engineering, Zhejiang University · School of Automation, Central South University · Trust and Safety Team, TikTok Sydney, ByteDance Inc. +3

  4. DualDynamics: Synergizing Implicit and Explicit Methods for Robust Irregular Time Series Analysis

    Jan 10, 2024YongKyung Oh, Dong-Young Lim, Sungil KimIrregular Time-Series ModelingTime Series Imputation

    Medical & Imaging Informatics (MII) Group, University of California, Los Angeles (UCLA), CA, USA · Department of Industrial Engineering, Ulsan National Institute of Science and Technology (UNIST), Republic of Korea · Artificial Intelligence Graduate School, Ulsan National Institute of Science and Technology (UNIST), Republic of Korea

  5. ResNLS: An Improved Model for Stock Price Forecasting

    Dec 2, 2023Yuanzhe Jia, Ali Anaissi, Basem SuleimanStock Price ForecastingTime Series Forecasting

    University of Sydney, Sydney, NSW, Australia. · University of New South Wales, Sydney, NSW, Australia.

  6. Realised Volatility Forecasting: Machine Learning via Financial Word Embedding

    Aug 1, 2021Eghbal Rahimikia, Stefan Zohren, Ser-Huang PoonFinancial ForecastingTime Series Forecasting

    Alliance Manchester Business School at the University of Manchester · Department of Engineering Science, University of Oxford · Alliance Manchester Business School, University of Manchester