Latest Time Series Research Papers

The newest Time Series papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks Time Series so you don’t have to: get the standout work delivered to your inbox every morning, with 2-sentence summaries and the option to chat with any paper.

Recent papers

• Methodological Evaluation and Time-Series Forecasting for Process-Control System Adoption in Tanzania
  Grace Mushi, Josephine Mwita, Juma Kondo, Rajabu Mwinyimvua · Zenodo (CERN European Organ... · Nov 16, 2026

  { "background": "The adoption of advanced process-control systems in industrial sectors within developing economies is a critical driver of productivity and quality. However, there is a paucity of robust, quantitative frameworks for modelli…

• Time-Series Forecasting Model Evaluation for Municipal Water Systems in Tanzania: An Efficiency Gain Assessment
  Sally Harvey, Amani Mwalimu, Marilyn Kinyanjui · Zenodo (CERN European Organ... · Nov 6, 2026

  Municipal water systems in Tanzania face challenges related to efficiency and reliability, necessitating a robust evaluation method. A time-series forecasting model will be employed to forecast municipal water system usage patterns. Robust …

• Penerapan Metode Fuzzy Time Series Dalam Memprediksi Jumlah Peserta Didik Baru
  Endra Ary Prasasty Marpaung, Martua Sitorus, Dedi Candro Parulian Sinaga, Rodiah Hannum Lubis et al. · Jurnal Sistem Informasi dan... · Nov 2, 2026

  Perencanaan penerimaan peserta didik baru merupakan aspek kritis dalam manajemen pendidikan karena berpengaruh langsung terhadap alokasi sumber daya, kualitas layanan, dan keberlanjutan institusi. Ketidakpastian jumlah pendaftar setiap tahu…

• Proposing Regression and Machine Learning Methods and Auto-Regressive Integrated Moving Average Time Series in Predicting the Price of Ripple Digital Currency
  Alireza Gharibshahian, Mohammad Reza Dehghani · Business Marketing and Fina... · Nov 1, 2026

  This study proposes a hybrid forecasting approach for predicting the price of Ripple's digital currency, XRP, by integrating autoregressive integrated moving average (ARIMA) time series models with machine learning (ML) techniques, includin…

• Research on physically guided deep learning for spatiotemporal streamflow prediction in data-scarce river basins
  Zhaocai WANG, Jiachen Kong, Guohua He, Junhao WU · DOAJ (DOAJ: Directory of Op... · Nov 1, 2026

  High-precision streamflow forecasting is fundamental to water resources management and flood disaster mitigation. However，in data-scarce basins，traditional hydrological models are difficult to calibrate，limiting forecasting accuracy; deep l…

• Development and Functional Testing of a Web Based Dengue Weather Alert Prototype with WhatsApp Notifications in Palu, Indonesia
  Iqbal Hidayat Syahril, Kurnia Hayat Hag, Diah Mutiarasari, Rizan Machmud · Healthy Tadulako Journal (J... · Oct 31, 2026

  Background: Weather information may support dengue preparedness, although software functionality alone cannot establish forecasting accuracy. Objectives: This study developed a web based weather alert prototype integrating meteorological da…

• Homebrewed SLMs: Autoregressive and Masked Diffusion Language Models Built at Home for Portuguese
  Pedro Carvalho Brom · arXiv · Oct 29, 2026

  Building a language model of one's own no longer requires a data centre. This tutorial trains one from scratch, tokenizer included, on one consumer GPU, and shows how to tell whether it is good for the tasks of a specialist domain. It propo…

• IMPACT: Inverter-Informed Multimodal Learning for Photovoltaic Generation Forecasting
  Anushka Katiyar, Cong Feng, Jing Wang, Tristan Liu et al. · arXiv · Oct 29, 2026

  This work develops a physics-informed multimodal deep learning framework for 10- to 110-minute-ahead probabilistic photovoltaic power forecasting. The forecasting model is built on a four-branch architecture that separately encodes plant-le…

• Multistep and Multivariate Time Series Explainability with GradientSHAP: A Wind Power Case Study
  Robbie Southam, Zhong Fan, Ke Li · arXiv · Oct 29, 2026

  As the effects of climate change become increasingly apparent, the demand for renewable energy sources such as wind power continues to grow. However, renewables' variable and intermittent nature presents significant challenges in their inte…

• Temporal Trends of Malaria Incidence in Southwestern Nigeria: An ARIMA Analysis of Hospital-Based Surveillance Data
  Olutunde Michael Ajao, Oyindamola John Samson, Felix Azuka Okolie, Alfred Ayo Ayenigba et al. · Asian Journal of Science Te... · Oct 29, 2026

  Understanding temporal patterns in reported malaria cases is essential for surveillance, healthcare planning, and resource allocation. This study examined trends in malaria case counts and developed a forecasting model using hospital-based …

• The PELT-ensemble NARX Hybrid Method for Short-Term Forecasting Time Series Data with Change Points and Its Application
  Hermansah Hermansah · Journal of Applied Data Sci... · Oct 29, 2026
• Pneuma: A Large-Scale Real-World Time Series Benchmark for Commercial Aircraft Predictive Maintenance
  Jiafeng Lin, Yutian Zhang, Huan Zhang, Yuxuan Wang et al. · arXiv · Oct 27, 2026

  Predictive maintenance is vital for commercial aviation, yet progress remains hindered by the lack of public, large-scale real-world datasets. As a result, most methods are developed and evaluated on proprietary data, limiting reproducibili…

• Bayesian local clustering of age-period mortality surfaces across multiple countries
  Giovanni Romanò, Emanuele Aliverti, Daniele Durante · Journal of the Royal Statis... · Oct 27, 2026

  Abstract Although classical mortality modelling literature has focused on single countries in isolation, recent contributions have progressively moved toward joint statistical models for multiple countries. Besides favouring borrowing of in…

• TRACE: Memory-Efficient Time Series Class-Incremental Learning via Temporal-Spectral Reconstruction
  Junhui Mao, Junru Zhang, Liming Ye, Cheng Peng et al. · arXiv · Oct 26, 2026

  Web-connected sensing applications continuously generate multivariate time series with evolving behavioral categories, making Time Series Class-Incremental Learning (TSCIL) essential for adapting to new classes without forgetting previously…

• ExCal: Optimization-Free Multivariate Time Series Anomaly Detection via Excess Calibration
  Kadir-Kaan Özer, René Ebeling, Markus Enzweiler · arXiv · Oct 26, 2026

  We introduce ExCal, a normal-only multivariate time series anomaly detector that combines raw state retrieval, a signal/derivative subspace, and robust statistics across temporal scales. Each expert map receives a high normality floor from …

• Explainable Prediction of High-Speed Solar Wind Streams driven by Coronal Holes via Topological Data Analysis
  Francesco Marchetti, Sabrina Guastavino, Daniel Collin · arXiv · Oct 26, 2026

  High-speed solar wind streams (HSSs) originating from coronal holes (CHs) are an important driver of recurrent geomagnetic activity. Since HSSs are associated to coronal structures visible in extreme-ultraviolet observations, this provides …

• Efficient probabilistic weather forecasting at quarter-degree resolution with two-sided transport attention
  Junu Choi, Youngjoon Hong · arXiv · Oct 26, 2026

  High-resolution probabilistic weather forecasting remains computationally demanding. Here we present Astra, a 0.25$^\circ$ ensemble model trained on eight H200 GPUs. It combines Kantorovich Attention, a two-sided transport attention in whic…

• Evaluating Bayesian deep learning for robust biomedical time-series classification under distribution shift: A sleep scoring case study
  Mads Birch Sørensen, Ulrike Richter, Francois Gastambide, Michael Riis Andersen · arXiv · Oct 26, 2026

  In biomedical time-series classification, machine learning models often suffer from deteriorating accuracy and calibration due to distribution shift. We investigate to what degree Bayesian-inspired deep learning improves robustness through …

• SPARSE-TIME: SALIENCY-PROJECTED LOW-RANK TEMPORAL MODELING FOR EFFICIENT AND INTERPRETABLE TIME SERIES PREDICTION
  Khondakar Ashik Shahriar · Engineering Research Express · Oct 26, 2026

  Abstract Time series forecasting is traditionally dominated by sequence-based architectures such as recurrent neural networks and attention mechanisms, which process all time steps uniformly and often incur substantial computational cost. H…

• Outbreak Cast-ML: Short-Term Disease Outbreak Analysis and Case Forecasting Using Machine Learning
  Mr. M. D. Shinde Shubham Sudam Londhe, Prof. J. R. Mahajan · International Journal of Ad... · Oct 25, 2026

  Timely estimation of disease trends can help public-health teams plan testing, staffing, beds, medicines, and community communication. This paper presents OutbreakCast-ML, a reproducible machine-learning workflow for analysing reported outb…

• Multi-scale LSTM forecasting with attention-based feature fusion for anomaly detection in multivariate monitoring data
  Wei Liu, Meizhao Liu, Fei Xia, Zhong Qiuyuan et al. · Journal of Engineering and ... · Oct 24, 2026

  Abstract Engineering systems such as power equipment and industrial processes are monitored through multivariate time series whose anomalies appear at different temporal scales, from short fluctuations to deviations that persist over a whol…

• Evaluating regression models and machine learning for long-term energy demand forecasting – Germany
  Munzer Osman, Maria Mercedes Movsessian · Energy Reports · Oct 24, 2026

  Reliable long-term energy demand forecasting underpins energy-system planning and decarbonization policy design, yet most established methods are calibrated for short horizons and may not capture structural change. This study compares four …

• Probabilistic subseasonal rainfall emulation with diffusion models: evaluating tail-risk calibration for flood early-warning triggers across data-sparse river basins
  Halima Mwinyi Juma · Zenodo (CERN European Organ... · Oct 24, 2026

  Subseasonal rainfall forecasts underpin flood early-warning triggers in river basins where rain-gauge networks are sparse, yet the probabilistic calibration of such forecasts in the upper tail — the region that determines whether a trigger …

• Sustainability of TSFM Deployment and Pretraining: From Carbon Footprint Analysis to Mitigation via Pruning
  Cécile Luc, Nathanaël Lemonnier, Ievgen Redko · arXiv · Oct 23, 2026

  Time Series Foundation Models (TSFMs) offer strong zero-shot forecasting capabilities, but their high pretraining cost raises a practical sustainability question: does this upfront investment pay off over a full deployment lifecycle, compar…

• ARENA: Active Region attENtion Aggregation for interpretable solar flare prediction
  Luis Bauer, Annalena Kofler, Andrés Asensio Ramos, Aparna Venkataramanasastry et al. · arXiv · Oct 23, 2026

  Predicting solar flares is a challenging space-weather forecasting problem with potentially significant impacts on Earth's upper atmosphere, including disruptions to radio communications. We propose Active Region attENtion Aggregation (AREN…

• Forecasting India’s Milk Production using Time-series Econometrics and Machine Learning Models: A Comparative Assessment
  Lovepreet Singh, Surbhi Bansal, Shruti Chopra, Varinder Pal Singh et al. · Asian Journal of Dairy and ... · Oct 22, 2026

  Background: Milk production is a major component of India’s agricultural economy, contributing significantly to food security, rural livelihoods and economic development. Reliable forecasts of future milk production are essential for effect…

• SCDM: Scalable Causal Discovery in Nonlinear Temporal Systems with Meta-Learning
  Jingbo Wang, Kegeng Tang, Zihao Wang, Shaogang Ren · arXiv · Oct 21, 2026

  Causal discovery from nonlinear multivariate time series is challenging in high-dimensional systems, where the number of candidate directed relations grows quadratically with the number of variables. Existing methods are often limited by ta…

• Leveraging Time-Series Foundation Models for Financial Crime Detection
  Danae Natalie Savvidi, H. Çağrı Bilgi, Kubilay Atasu, Arie van Deursen · arXiv · Oct 21, 2026

  Financial crime detection requires identifying illicit activity within large transaction networks, where suspicious behaviour is rare and unfolds over time. Existing graph-based methods capture relationships among accounts but typically und…

• Dynamics-informed deep learning for predicting extreme events
  Eirini Katsidoniotaki, Themistoklis P. Sapsis · Proceedings of the Royal So... · Oct 21, 2026

  Abstract Predicting extreme events in high-dimensional chaotic dynamical systems remains a fundamental challenge because these events are rare, intermittent and often driven by transient dynamical mechanisms that are difficult to identify f…

• Turning TS2Vec into a Foundation Model for Time Series Classification
  Vasilii Feofanov, Zhenghan Tai, Andrei Ermakov, Irina Rish · arXiv · Oct 20, 2026

  TS2Vec is one of the strongest self-supervised methods for time series classification, introduced in 2022. It is designed to be trained anew on every dataset: self-supervised pre-training followed by linear probing. Since then, the field ha…