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Entropy
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Advances in Time Series Analysis: Methods, Applications and Emerging Trends
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Advances in Time Series Analysis: Methods, Applications and Emerging Trends

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Signal and Data Analysis".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 1384

Special Issue Editors

Dr. Cun Ji

E-Mail Website
Guest Editor
School of Information Science and Engineering, Shandong Normal University, Jinan, China
Interests: time series classification; intelligent manufacturing; data mining; big data; feature selection
Dr. Bo Li

E-Mail Website
Guest Editor
College of Arts, Business, Law, Education, and Information Technology, Victoria University, Melbourne, Australia
Interests: artificial intelligence; mobile edge computing; cyber security; time series classification
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid advancement of sensing technologies, time series data has become deeply integrated into a wide range of real-world applications. From medical domains such as electrocardiogram monitoring, to the continuous streams of signals generated by smart devices, to the real-time analysis of traffic flow in smart city infrastructures, time series data plays an essential and irreplaceable role. These data not only offer critical insights into the dynamic behavior of complex systems but also form the foundation for informed decision-making, prediction, and optimization. At the same time, the inherent characteristics of time series—large volume, high dimensionality, and rapid updates—pose significant challenges for effective analysis. For example, when confronted with massive datasets, extracting meaningful information while suppressing noise and redundancy has become a central concern for researchers.

In recent years, growing academic interest has propelled the field of time series analysis forward. Increasing attention has been devoted to leveraging the diverse characteristics of time series—such as trends, seasonality, volatility, slope entropy, permutation entropy, multiscale entropy, and shapelets—to achieve deeper and more nuanced understanding. These features not only assist in uncovering latent patterns but also contribute to enhancing analytical accuracy and interpretability, enabling more robust responses to complex real-world phenomena.

We hope this Special Issue, “Advances in Time Series Analysis: Methods, Applications and Emerging Trends”, will serve as a platform for researchers to share innovative methodologies, emerging theories, and impactful applications, further advancing the state of the art in this vibrant and rapidly evolving field. The topics include but are not limited to the following:

  • Feature-based time series analysis;
  • Deep learning for time series understanding;
  • Time series forecasting and anomaly detection;
  • Multimodal and multivariate time series analysis;
  • Explainability and interpretability in time series models;
  • Real-world applications of time series analysis;
  • Scalable and efficient time series analytics.

Dr. Cun Ji
Dr. Bo Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • data mining
  • time series
  • entropy
  • feature extraction
  • classification

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Published Papers (2 papers)

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Research

25 pages, 1034 KB  
Article
A Benchmark for Entropy Estimators
by Lucio M. Calcagnile, Angelo Di Garbo and Stefano Galatolo
Entropy 2026, 28(3), 311; https://doi.org/10.3390/e28030311 - 10 Mar 2026
Viewed by 446
Abstract
This study assessed the performance of several entropy estimators for numerical time series and symbolic data on non-trivial one-dimensional dynamical systems whose Kolmogorov–Sinai entropy is known with certified accuracy: recent computer-assisted proof techniques provide rigorous values together with explicit error bounds. We considered [...] Read more.
This study assessed the performance of several entropy estimators for numerical time series and symbolic data on non-trivial one-dimensional dynamical systems whose Kolmogorov–Sinai entropy is known with certified accuracy: recent computer-assisted proof techniques provide rigorous values together with explicit error bounds. We considered four classes of interval maps, including piecewise expanding maps with and without a Markov partition and an intermittent Pomeau–Manneville map, and generated long orbits for each system. We then compared the certified entropy with the output of widely used estimators: Approximate Entropy, Sample Entropy, Permutation Entropy, a symbolic Plug-In estimator of the entropy rate, and the Non-Sequential Recursive Pair Substitution (NSRPS) method (the latter two with Grassberger-type bias correction). Our experiments reveal substantial, dynamics-dependent differences in accuracy and robustness. In particular, Approximate Entropy and the symbolic methods (Plug-In and NSRPS) consistently yielded estimates within the rigorous error bars across all systems, whereas Sample Entropy showed a marked systematic underestimation, and Permutation Entropy exhibited large biases, especially for expanding maps without a Markov partition. The resulting benchmark provides a quantitative testbed for evaluating entropy estimation techniques in deterministic dynamical systems. Full article
(This article belongs to the Special Issue Advances in Time Series Analysis: Methods, Applications and Emerging Trends)
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27 pages, 2454 KB  
Article
Event-Driven Spiking Neural Networks for Private Vehicle Parking Prediction
by Wangchen Long and Jie Chen
Entropy 2026, 28(3), 253; https://doi.org/10.3390/e28030253 - 25 Feb 2026
Viewed by 433
Abstract
Predicting the future parking locations and durations of private vehicles using vehicular edge devices is critical for real-time intelligent transportation services, ranging from instant point-of-interest recommendations to dynamic route planning. Advanced deep neural networks like Transformers demonstrate exceptional performance in mobility prediction; however, [...] Read more.
Predicting the future parking locations and durations of private vehicles using vehicular edge devices is critical for real-time intelligent transportation services, ranging from instant point-of-interest recommendations to dynamic route planning. Advanced deep neural networks like Transformers demonstrate exceptional performance in mobility prediction; however, their heavy reliance on dense matrix multiplication makes them unsuitable for real-time applications on vehicular edge devices. Spiking neural networks offer a potential solution due to their asynchronous event-driven characteristics and low power consumption. However, existing spiking neural networks face three fundamental challenges: (1) handling heterogeneous inter-event intervals; (2) mitigating quantization errors in regression tasks under limited simulation steps; and (3) efficiently regulating information flow based on external contexts. To address these challenges, we propose an event-driven spiking neural network for private vehicle parking prediction called Spark. First, we design a Time-Adaptive Leaky Integrate-and-Fire neuron with a lookup table-based decay mechanism to efficiently model variable inter-event intervals. Second, an accumulate-based readout strategy is introduced to mitigate quantization errors by integrating discrete spike trains into continuous output values for high-precision regression. Third, a Spiking Contextual Gating module is proposed to selectively regulate spiking information flow across channels based on environmental context. These components are integrated into a unified architecture that maintains high prediction accuracy while remaining computationally efficient. Extensive experiments on real-world datasets demonstrate that Spark achieves an effective balance between prediction accuracy and computational efficiency compared to baselines. Full article
(This article belongs to the Special Issue Advances in Time Series Analysis: Methods, Applications and Emerging Trends)
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