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.
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- Financial data source preparation and analysis as the initial stage of stochastic time series modeling by machine learning techniquesYurchenko Yuriy, Oleksandr Zakovorotnyi · Zenodo (CERN European Organ... · Nov 28, 2026
Proceedings of the Scientific Conference "The 13th International Scientific and Practical Online Conference of Young Scientists and Students ‘Contemporary Problems of Automation and Control’".The conference talk presents a structured approa…
- Financial data source preparation and analysis as the initial stage of stochastic time series modeling by machine learning techniquesYurchenko Yuriy, Oleksandr Zakovorotnyi · Zenodo (CERN European Organ... · Nov 28, 2026
Proceedings of the Scientific Conference "The 13th International Scientific and Practical Online Conference of Young Scientists and Students ‘Contemporary Problems of Automation and Control’".The conference talk presents a structured approa…
- Methodological Evaluation and Time-Series Forecasting for Process-Control System Adoption in TanzaniaGrace 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…
- Methodological Evaluation and Time-Series Forecasting for Process-Control System Adoption in TanzaniaGrace Mushi, Josephine Mwita, Juma Kondo, Rajabu Mwinyimvua · Open MIND · 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 AssessmentSally Harvey, Amani Mwalimu, Marilyn Kinyanjui · Open MIND · 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 …
- Time-Series Forecasting Model Evaluation for Municipal Water Systems in Tanzania: An Efficiency Gain AssessmentSally 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 …
- Did Recent Inflation Reflect a Nonlinear Phillips Curve?Paul Beaudry, Chenyu Hou, Franck Portier · UCL Discovery (University C... · Oct 1, 2026
To examine the potential role of a nonlinear Phillips curve in explaining recent US inflation, we combine evidence from both crosscity variation and aggregate time series data.A central challenge relates to how best to control for the poten…
- Methodological Evaluation and Reliability Forecasting for District Hospital Systems in Ethiopia: A Time-Series Meta-AnalysisMeklit Abebe, Tewodros Getachew · Open MIND · Sep 26, 2026
District hospitals are critical nodes in healthcare delivery, yet systematic evaluations of their operational reliability in low-resource settings are scarce. Existing assessments often lack robust, predictive methodologies to inform proact…
- Methodological Evaluation and Reliability Forecasting for District Hospital Systems in Ethiopia: A Time-Series Meta-AnalysisMeklit Abebe, Tewodros Getachew · Zenodo (CERN European Organ... · Sep 26, 2026
District hospitals are critical nodes in healthcare delivery, yet systematic evaluations of their operational reliability in low-resource settings are scarce. Existing assessments often lack robust, predictive methodologies to inform proact…
- PREDICTING NIGERIA'S ECONOMIC GROWTH THROUGH TIME SERIES ANALYSIS: A 1960–2015 STUDYOlumide Samuel Adegboye · Zenodo (CERN European Organ... · Aug 24, 2026
This study focuses on forecasting Nigeria’s gross domestic product (GDP) using time series analysis for the period 1960–2015. Economic growth remains a central objective for governments, with GDP serving as a key indicator of national econo…
- PREDICTING NIGERIA'S ECONOMIC GROWTH THROUGH TIME SERIES ANALYSIS: A 1960–2015 STUDYOlumide Samuel Adegboye · Zenodo (CERN European Organ... · Aug 24, 2026
This study focuses on forecasting Nigeria’s gross domestic product (GDP) using time series analysis for the period 1960–2015. Economic growth remains a central objective for governments, with GDP serving as a key indicator of national econo…
- Longitudinal Evaluation of Public Health Surveillance Methodologies in Nigeria: A Time-Series Forecasting Model for Risk Reduction, 2000–2026Adebayo Adeyemi, Chinwe Okonkwo · Open MIND · Aug 8, 2026
{ "background": "Public health surveillance systems in Nigeria have historically relied on lagged, aggregate data, limiting proactive risk management. A critical methodological gap exists in evaluating these systems' predictive performance …
- Longitudinal Evaluation of Public Health Surveillance Methodologies in Nigeria: A Time-Series Forecasting Model for Risk Reduction, 2000–2026Adebayo Adeyemi, Chinwe Okonkwo · Zenodo (CERN European Organ... · Aug 8, 2026
{ "background": "Public health surveillance systems in Nigeria have historically relied on lagged, aggregate data, limiting proactive risk management. A critical methodological gap exists in evaluating these systems' predictive performance …
- Beyond Sufficiency: Time Series Explanation with Counterfactual NecessityHongnan Ma, Yiwei Shi, Mengyue Yang, Weiru Liu · arXiv · Jul 23, 2026
Faithful explanations of time-series classifiers should identify subsequences that are not only sufficient to preserve a black-box model's prediction, but also necessary for maintaining it. However, existing sufficiency-oriented methods can…
- PIER: Physics-Informed Environmental Retrieval for Time-Series ModelingShiyuan Luo, Runlong Yu, Chonghao Qiu, Yue Qin et al. · arXiv · Jul 22, 2026
Accurate modeling of environmental systems is fundamental to scientific understanding and decision-making, yet remains challenging because observations are limited and physical dynamics vary across systems. Retrieval-augmented approaches of…
- In-Context Time Series Classification with Random Convolutional FeaturesJoscha Cüppers, Jilles Vreeken · arXiv · Jul 21, 2026
Time series classification is central to domains like medical signal analysis, industrial monitoring, and sensor-based activity recognition, where class information manifests as localized shapes, specific frequencies, temporal shifts, or co…
- The Spectrum Is Not Enough: When Context Helps Time-Series ForecastingMert Onur Cakiroglu, Mehmet Dalkilic, Hasan Kurban · arXiv · Jul 14, 2026
A growing family of indices scores how predictable a series is from its spectrum. Practitioners increasingly read these scores as answering a different question: whether \emph{adding context}, a longer lookback, a retrieval plug-in, or a pr…
- Ensemble Controlled-Flow Filtering for Implicit Data AssimilationZhuoyuan Li, Yue Zhao, Ming Li · arXiv · Jul 14, 2026
Data assimilation estimates the state of a dynamical system from model forecasts and incoming observations. Many observation mechanisms, however, are many-to-one, implicit, non-smooth, or accessible only through simulation, and need not pro…
- Robustness of Deep Learning Models for PV Power Forecasting under NWP Forecast Errors: A Spatiotemporal and Physically Interpretable AnalysisDandan Chen, Yan Zhao, Xuepeng Chen · arXiv · Jul 14, 2026
Engineering use of AI forecasting models requires not only high nominal accuracy but also predictable behavior under uncertain inputs. In photovoltaic (PV) forecasting, this requirement is especially challenging because numerical weather pr…
- Efficient Sequential Calibration with $O(T^{2/3-ε})$ Error BoundZihan Zhang · arXiv · Jul 14, 2026
We study the online binary sequential calibration problem. A recent breakthrough by \citet{dagan2024breaking} overcomes the classical \(T^{2/3}\) barrier for calibration error. Building on this result, we present an efficient randomized for…
- PHINN-EEG: Topological Time-Series Analysis of Dream-State EEG -- Dynamic Betti Curves for Dream Content Classification and Topology-Conditioned Neural Signal SynthesisRen Takahashi, Emre Yusuf, Jayabrata Bhaduri · arXiv · Jul 10, 2026
Current electroencephalography (EEG)-based dream detection relies on power spectral density (PSD) and statistical moment features, achieving a state-of-the-art area under the receiver operating characteristic curve (AUC) of approximately 0.…
- GatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series ForecastingQitai Tan, Ruiwen Gu, Yilin Su, Mo Li et al. · arXiv · Jul 10, 2026
Time series forecasting requires models to capture diverse, often mutually exclusive, temporal dynamics, from smooth trend continuation to nonstationary drift and strict phase-aligned recurrence. While recent deep learning models have impro…
- Terminal Dimension Reduction for Time Series with ApplicationsAlexander Munteanu, Matteo Russo, David Saulpic, Chris Schwiegelshohn · arXiv · Jul 10, 2026
Terminal embeddings have emerged as a powerful tool for dimension reduction. Given a set of points $P\subset \mathbb{R}^d$, a terminal embedding is a mapping $f:\mathbb{R}^d\rightarrow \mathbb{R}^t$ that preserves the pairwise distance betw…
- ALER-TI: Aligned Latent Embedding Retrieval for Time Series ImputationXuan-Thong Truong, Trung-Kien Le, Tung Kieu, Thi-Thu Nguyen et al. · arXiv · Jul 8, 2026
Deep learning has significantly advanced time series imputation, yet most existing architectures primarily rely on localized temporal context within the corrupted input sequence. This reliance can be limiting in real-world scenarios, where …
- TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific ScenariosHong Lyu, Mingru Yang, Qianhua He, Yanxiong Li et al. · arXiv · Jul 7, 2026
There are some datasets of varying scales for audio classification (AC) applied to different tasks. However, annotated data is limited for most scenarios, such as domestic environments. To address this challenge, we propose an $\textbf{A}$u…
- Extreme Adaptive Transformer for Time Series ForecastingSanjeev Shrestha, Hui Liu, Yifan Zhang · arXiv · Jul 2, 2026
Time series forecasting remains challenging when the underlying data contain rare but critical extreme events. This issue is particularly important in hydrologic forecasting, where streamflow distributions are often highly skewed and extrem…
- TiRex-2: Generalizing TiRex to Multivariate Data and StreamingPatrick Podest, Marco Pichler, Elias Bürger, Levente Zólyomi et al. · arXiv · Jul 1, 2026
We introduce TiRex-2, a recurrent xLSTM-based time series foundation model that generalizes the univariate TiRex to multivariate forecasting with both past and future covariates. Real-world forecasting is inherently sequential: observations…
- Decision-Aware Training for Sample-Based Generative ModelsKornelius Raeth, Nicole Ludwig · arXiv · Jul 1, 2026
Sample-based generative models are increasingly used for probabilistic forecasting in high-stakes decision settings, yet their training objectives are blind to the decision maker's cost structure. These models are commonly trained with stri…
- A Lightweight Self-Supervised Learning Framework for Multivariate Time Series using Hierarchical-JEPA on ECG DataSiwon Kim · arXiv · Jul 1, 2026
Data analysis in the medical domain often encounters scenarios involving a limited target dataset and a large, unannotated dataset with a general distribution. Under such circumstances, self-supervised learning (SSL) methods are highly effe…
- Disentangling Continuous-Time Latent Dynamics: Identifiability of Latent SDEs via Diffusion ShiftsYuanyuan Wang, Wenjie Wang, Haoxuan Li, Mingming Gong et al. · arXiv · Jun 26, 2026
Causal representation learning for time series has developed strong identifiability results in discrete-time latent causal models, but identifiability in continuous-time latent stochastic differential equation (SDE) models remains largely o…