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Advanced AI and Machine Learning Techniques for Time Series Analysis and Pattern Recognition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 October 2025 | Viewed by 5181

Special Issue Editors

Dr. Antonio Pagliaro

E-Mail Website
Guest Editor
Istituto Nazionale di Astrofisica INAF IASF Palermo, Via Ugo La Malfa 153, 90146 Palermo, Italy
Interests: artificial intelligence; computer science; machine learning; deep learning; computer vision; high-energy astrophysics
Special Issues, Collections and Topics in MDPI journals
Dr. Pierluca Sangiorgi

E-Mail Website
Guest Editor
Istituto Nazionale di Astrofisica INAF IASF Palermo, Via Ugo La Malfa 153, 90146 Palermo, Italy
Interests: software engineering; computer-aided system; semantic analysis; control software system; high-energy astrophysics
Special Issues, Collections and Topics in MDPI journals
Dr. Antonio Alessio Compagnino

E-Mail Website
Guest Editor Assistant
Istituto Nazionale di Astrofisica INAF IASF Palermo, Via Ugo La Malfa 153, 90146 Palermo, Italy
Interests: artificial intelligence; machine learning; deep learning; high-energy astrophysics

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore cutting-edge artificial intelligence and machine learning approaches for analyzing time series data and recognizing complex patterns across diverse domains. We invite original research articles and comprehensive review papers that advance the theoretical foundations or practical applications of deep learning architectures, transformer models, reinforcement learning, and hybrid AI systems specifically designed for temporal data challenges. Topics of interest include, but are not limited to, the following: novel architectures for multivariate time series forecasting, anomaly detection in streaming data, interpretable models for temporal pattern discovery, transfer learning for limited time series datasets, and AI techniques for real-time decision-making systems. We particularly welcome interdisciplinary submissions demonstrating innovative applications in healthcare, finance, industrial monitoring, environmental science, or smart infrastructure.

Dr. Antonio Pagliaro
Dr. Pierluca Sangiorgi
Guest Editors

Dr. Antonio Alessio Compagnino
Guest Editor Assistant

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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • artificial intelligence
  • machine learning
  • time series analysis
  • pattern recognition
  • predictive modeling

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Published Papers (5 papers)

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Editorial

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6 pages, 163 KiB  
Editorial
Advanced AI and Machine Learning Techniques for Time Series Analysis and Pattern Recognition
by Antonio Pagliaro, Antonio Alessio Compagnino and Pierluca Sangiorgi
Appl. Sci. 2025, 15(6), 3165; https://doi.org/10.3390/app15063165 - 14 Mar 2025
Viewed by 1351
Abstract
Time series analysis and pattern recognition are cornerstones for innovation across diverse domains. In finance, these techniques enable market prediction and risk assessment. Astrophysicists use them to detect various phenomena and analyze data. Environmental scientists track ecosystem changes and pollution patterns, while healthcare [...] Read more.
Time series analysis and pattern recognition are cornerstones for innovation across diverse domains. In finance, these techniques enable market prediction and risk assessment. Astrophysicists use them to detect various phenomena and analyze data. Environmental scientists track ecosystem changes and pollution patterns, while healthcare professionals monitor patient vitals and disease progression. Transportation systems optimize traffic flow and predict maintenance needs. Energy providers balance grid loads and forecast consumption. Climate scientists model atmospheric changes and extreme weather events. Cybersecurity experts identify threats through anomaly detection in network traffic patterns. This editorial introduces this Special Issue, which explores state-of-the-art AI and machine learning (ML) techniques, including Long Short-Term Memory (LSTM) networks, Transformers, ensemble methods, and AutoML frameworks. We highlight innovative applications in data-driven finance, astrophysical event reconstruction, cloud masking, and healthcare monitoring. Recent advancements in feature engineering, unsupervised learning frameworks for cloud masking, and Transformer-based time series forecasting demonstrate the potential of these technologies. The papers collected in this Special Issue showcase how integrating domain-specific knowledge with computational innovations provides a pathway to achieving higher accuracy in time series analysis across various scientific disciplines. Full article
(This article belongs to the Special Issue Advanced AI and Machine Learning Techniques for Time Series Analysis and Pattern Recognition)

Research

Jump to: Editorial, Review

32 pages, 4711 KiB  
Article
Anomaly Detection in Elderly Health Monitoring via IoT for Timely Interventions
by Cosmina-Mihaela Rosca and Adrian Stancu
Appl. Sci. 2025, 15(13), 7272; https://doi.org/10.3390/app15137272 - 27 Jun 2025
Viewed by 367
Abstract
As people age, more careful health monitoring becomes increasingly important. The article presents the development and implementation of an integrated system for monitoring the health of elderly individuals using Internet of Things (IoT) technology and a wearable bracelet to continuously collect vital data. [...] Read more.
As people age, more careful health monitoring becomes increasingly important. The article presents the development and implementation of an integrated system for monitoring the health of elderly individuals using Internet of Things (IoT) technology and a wearable bracelet to continuously collect vital data. The device integrates MAX30100 sensors for heart rate monitoring and MPU-6050 for step counting and sleep quality analysis (deep and superficial sleep). The collected data for average heart rate (AR), minimum (mR), maximum (MR), number of steps (S), deep sleep time (DST), and superficial sleep time (SST) is processed in real-time through a health anomaly detection algorithm (HADA), based on the dimensionality reduction method using PCA. The system is connected to the Azure cloud infrastructure, ensuring secure data transmission, preprocessing, and the automatic generation of alerts for prompt medical interventions. Studies conducted over two years demonstrated a sensitivity of 100% and an accuracy of 98.5%, with a tendency to generate additional alerts to avoid overlooking critical events. The results outline the importance of personalizing the analysis, adapting algorithms to individual characteristics, and the system’s potential to prevent medical complications and improve the quality of life for elderly individuals. Full article
(This article belongs to the Special Issue Advanced AI and Machine Learning Techniques for Time Series Analysis and Pattern Recognition)
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10 pages, 16733 KiB  
Article
Coal Mine Water Inflow Prediction Model Based on Multi-Factor Pearson Correlation Analysis
by Liang Ma, Zaibing Liu, Weiming Chen, Junjie Hu, Hongjian Ye, Tao Fan and Lin An
Appl. Sci. 2025, 15(12), 6600; https://doi.org/10.3390/app15126600 - 12 Jun 2025
Viewed by 295
Abstract
Since geological structures around coal mines are complex, sudden coal mine water inflow is seriously threatening coal mining safety. To improve the accuracy of predicting coal mine water inflow, a multi-source dataset is collected to develop a coal mine water inflow prediction model [...] Read more.
Since geological structures around coal mines are complex, sudden coal mine water inflow is seriously threatening coal mining safety. To improve the accuracy of predicting coal mine water inflow, a multi-source dataset is collected to develop a coal mine water inflow prediction model based on multi-factor Pearson correlation analysis, where a convolutional neural network and bidirectional long short-term memory neural network are adopted to extract features from time-series data. To validate the performance of the present prediction model, a case study is conducted, where the predicted coal mine water inflow is close to the collected coal mine water inflow. Meanwhile, compared to other prediction models, the present prediction model can predict the magnitude and development trend of coal mine water inflow in the next 8 h more accurately, where the mean absolute percentage error is 5.76% and the correlation coefficient is 0.922. Full article
(This article belongs to the Special Issue Advanced AI and Machine Learning Techniques for Time Series Analysis and Pattern Recognition)
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15 pages, 394 KiB  
Article
Time Series Anomaly Detection Using Signal Processing and Deep Learning
by Jana Backhus, Aniruddha Rajendra Rao, Chandrasekar Venkatraman and Chetan Gupta
Appl. Sci. 2025, 15(11), 6254; https://doi.org/10.3390/app15116254 - 2 Jun 2025
Viewed by 1031
Abstract
In this paper, we propose a two-step approach for time series anomaly detection that combines signal processing techniques with deep learning methods. In the first step, we apply a bandpass filter to the time series data to reduce noise and highlight relevant frequency [...] Read more.
In this paper, we propose a two-step approach for time series anomaly detection that combines signal processing techniques with deep learning methods. In the first step, we apply a bandpass filter to the time series data to reduce noise and highlight relevant frequency components, which enhances the signals in them. In the second step, we utilize a Functional Neural Network Autoencoder for anomaly detection, leveraging its ability to capture non-linear temporal relationships in the data. By learning a compact latent representation and remapping the filtered time series, the Autoencoder effectively identifies deviations from normal patterns, allowing us to detect anomalies. Our experiments on several benchmark datasets demonstrate that bandpass filtering consistently improves the performance of deep learning methods, including the Functional Neural Network Autoencoder, by refining the input data. Our proposed approach achieves superior performance of up to 20% in detecting anomalies, particularly in a time series with intricate structures, highlighting its potential for practical applications in multiple domains. Full article
(This article belongs to the Special Issue Advanced AI and Machine Learning Techniques for Time Series Analysis and Pattern Recognition)
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Review

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24 pages, 598 KiB  
Review
A Review of Anomaly Detection in Spacecraft Telemetry Data
by Asma Fejjari, Alexis Delavault, Robert Camilleri and Gianluca Valentino
Appl. Sci. 2025, 15(10), 5653; https://doi.org/10.3390/app15105653 - 19 May 2025
Viewed by 1787
Abstract
Telemetry data play a pivotal role in ensuring the success of spacecraft missions and safeguarding the integrity of spacecraft systems. Therefore, the timely detection and subsequent notification of any abnormal events related to the functionality of spacecraft subsystems are crucial to ensure their [...] Read more.
Telemetry data play a pivotal role in ensuring the success of spacecraft missions and safeguarding the integrity of spacecraft systems. Therefore, the timely detection and subsequent notification of any abnormal events related to the functionality of spacecraft subsystems are crucial to ensure their safe operation. In recent years, several anomaly detection methods have been developed to monitor spacecraft telemetry data and detect anomalies. This manuscript provides a comprehensive literature review of the existing anomaly detection methods for spacecraft telemetry data. It exposes the challenges faced by such systems, highlights the strengths and limitations of each anomaly detection method, and assesses and compares the performance of these approaches in detecting anomalies. Initial results show that GCN and TCN models have achieved promising precision up to 94%. The paper concludes with a series of recommendations and the potential research directions. Full article
(This article belongs to the Special Issue Advanced AI and Machine Learning Techniques for Time Series Analysis and Pattern Recognition)
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