Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.7
2.9
Journals
Electronics
Special Issues
Application of Time Series Analysis and Forecasting in Computer Science
share announcement

Application of Time Series Analysis and Forecasting in Computer Science

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 25 September 2024 | Viewed by 5204

Special Issue Editors

Dr. Hao Xue

E-Mail Website
Guest Editor
School of Computer Science and Engineering, University of New South Wales, Sydney 2052, Australia
Interests: spatio-temporal data modelling; time series forecasting; pedestrian trajectory prediction
Dr. Du Huynh

E-Mail Website
Guest Editor
Department of Computer Science and Software Engineering, The University of Western Australia, Perth 6009, Australia
Interests: computer vision; machine learning; object detection; visual tracking; image processing; pattern recognition

Special Issue Information

Dear Colleagues,

Time series analysis and forecasting have become increasingly important in recent years due to the rise of electronics, the Internet of Things (IoT), and smart sensor techniques. These advancements have led to the collection of vast amounts of time series data from various sources for different applications, such as finance, energy, healthcare, and environmental monitoring.

Machine learning and deep learning have become crucial tools in time series analysis and forecasting due to their ability to handle complex and high-dimensional data. These techniques have been used in various tasks of time series analysis, including prediction, classification, clustering, and anomaly detection. Although recent models such as LSTM and transformer-based approaches have been utilized for time series analysis and forecasting, many questions and challenges remain to be addressed in real-world application scenarios.

This Special Issue intends to bring together researchers and practitioners working on methods and techniques for time series analysis and forecasting in various application domains, such as intelligent transportation, geographic information systems, economics, finance, and environmental science. The Special Issue aims to showcase recent advances and applications of time series analysis and forecasting methodologies to real-world problems in these domains.

Dr. Hao Xue
Dr. Du Huynh
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 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. Electronics 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

  • time series analysis
  • time series forecasting
  • deep learning
  • probabilistic forecasting
  • forecasting applications
  • temporal data modeling

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

17 pages, 11999 KiB  
Article
Edge-Bound Change Detection in Multisource Remote Sensing Images
by Zhijuan Su, Gang Wan, Wenhua Zhang, Zhanji Wei, Yitian Wu, Jia Liu, Yutong Jia, Dianwei Cong and Lihuan Yuan
Electronics 2024, 13(5), 867; https://doi.org/10.3390/electronics13050867 - 23 Feb 2024
Viewed by 491
Abstract
Detecting changes in multisource heterogeneous images is a great challenge for unsupervised change detection methods. Image-translation-based methods, which transform two images to be homogeneous for comparison, have become a mainstream approach. However, most of them primarily rely on information from unchanged regions, resulting [...] Read more.
Detecting changes in multisource heterogeneous images is a great challenge for unsupervised change detection methods. Image-translation-based methods, which transform two images to be homogeneous for comparison, have become a mainstream approach. However, most of them primarily rely on information from unchanged regions, resulting in networks that cannot fully capture the connection between two heterogeneous representations. Moreover, the lack of a priori information and sufficient training data makes the training vulnerable to the interference of changed pixels. In this paper, we propose an edge-oriented generative adversarial network (EO-GAN) for change detection that indirectly translates images using edge information, which serves as a core and stable link between heterogeneous representations. The EO-GAN is composed of an edge extraction network and a reconstructive network. During the training process, we ensure that the edges extracted from heterogeneous images are as similar as possible through supplemented data based on superpixel segmentation. Experimental results on both heterogeneous and homogeneous datasets demonstrate the effectiveness of our proposed method. Full article
(This article belongs to the Special Issue Application of Time Series Analysis and Forecasting in Computer Science)
►▼ Show Figures

Figure 1

16 pages, 7435 KiB  
Article
A Time Series-Based Approach to Elastic Kubernetes Scaling
by Haibin Yuan and Shengchen Liao
Electronics 2024, 13(2), 285; https://doi.org/10.3390/electronics13020285 - 08 Jan 2024
Viewed by 885
Abstract
With the increasing popularity of cloud-native architectures and containerized applications, Kubernetes has become a critical platform for managing these applications. However, Kubernetes still faces challenges when it comes to resource management. Specifically, the platform cannot achieve timely scaling of the resources of applications [...] Read more.
With the increasing popularity of cloud-native architectures and containerized applications, Kubernetes has become a critical platform for managing these applications. However, Kubernetes still faces challenges when it comes to resource management. Specifically, the platform cannot achieve timely scaling of the resources of applications when their workloads fluctuate, leading to insufficient resource allocation and potential service disruptions. To address this challenge, this study proposes a predictive auto-scaling Kubernetes Operator based on time series forecasting algorithms, aiming to dynamically adjust the number of running instances in the cluster to optimize resource management. In this study, the Holt–Winter forecasting method and the Gated Recurrent Unit (GRU) neural network, two robust time series forecasting algorithms, are employed and dynamically managed. To evaluate the effectiveness, we collected workload metrics from a deployed RESTful HTTP application, implemented predictive auto-scaling, and assessed the differences in service quality before and after the implementation. The experimental results demonstrate that the predictive auto-scaling component can accurately predict the future trend of the metrics and intelligently scale resources based on the prediction results, with a Mean Squared Error (MSE) of 0.00166. Compared to the deployment using a single algorithm, the cold start time is reduced by 1 h and 41 min, and the fluctuation in service quality is reduced by 83.3%. This process effectively enhances the quality of service and offers a novel solution for resource management in Kubernetes clusters. Full article
(This article belongs to the Special Issue Application of Time Series Analysis and Forecasting in Computer Science)
►▼ Show Figures

Figure 1

20 pages, 9318 KiB  
Article
Unsupervised Anomaly Detection of Intermittent Demand for Spare Parts Based on Dual-Tailed Probability
by Kairong Hong, Yingying Ren, Fengyuan Li, Wentao Mao and Yangshuo Liu
Electronics 2024, 13(1), 195; https://doi.org/10.3390/electronics13010195 - 02 Jan 2024
Viewed by 708
Abstract
The quick development of machine learning techniques provides a superior capability for manufacturing enterprises to make effective decisions about inventory management based on spare parts demand (SPD) data. Since SPD sequences in practical maintenance applications usually show an intermittent distribution, it is not [...] Read more.
The quick development of machine learning techniques provides a superior capability for manufacturing enterprises to make effective decisions about inventory management based on spare parts demand (SPD) data. Since SPD sequences in practical maintenance applications usually show an intermittent distribution, it is not easy to represent the demand pattern of such sequences. Meanwhile, there are some aspects like manual report errors, environmental interference, sudden project changes, etc., that bring large and unexpected fluctuations to SPD sequences, i.e., anomalous demands. The inventory decision made based on the SPD sequences with anomalous demands is not trusted by enterprise engineers. For such SPD data, there are two great concerns, i.e., false alarms in which sparse demands are recognized to be anomalous and missing alarms in which the anomalous demands are categorized as normal due to their adjacent demands having extreme values. To address these concerns, a new unsupervised anomaly-detection method for intermittent time series is proposed based on a dual-tailed probability. First, the multi-way delay embedding transform (MDT) was applied on the raw SPD sequences to obtain higher-order tensors. Through Tucker tensor decomposition, the disturbance of extreme demands can be effectively reduced. For the reconstructed SPD sequences, then, the tail probability at each time point, as well as the empirical cumulative distribution function were calculated based on the probability of the demand occurrence. Second, to lessen the disturbance of sparse demand, the non-zero demand sequence was distilled from the raw SPD sequence, with the tail probability at each time point being calculated. Finally, the obtained dual-tailed probabilities were fused to determine the anomalous degree of each demand. The proposed method was validated on the two actual SPD datasets, which were collected from a large engineering manufacturing enterprise and a large vehicle manufacturing enterprise in China, respectively. The results demonstrated that the proposed method can effectively lower the false alarm rate and missing alarm rate with no supervised information provided. The detection results were trustworthy enough and, more importantly, computationally inexpensive, showing significant applicability to large-scale after-sales parts management. Full article
(This article belongs to the Special Issue Application of Time Series Analysis and Forecasting in Computer Science)
►▼ Show Figures

Figure 1

21 pages, 5387 KiB  
Article
Comparative Performance Analysis of RNN Techniques for Predicting Concatenated Normal and Abnormal Vibrations
by Ju-Hyung Lee and Jun-Ki Hong
Electronics 2023, 12(23), 4778; https://doi.org/10.3390/electronics12234778 - 25 Nov 2023
Cited by 1 | Viewed by 618
Abstract
We analyze the comparative performance of predicting the transition from normal to abnormal vibration states, simulating the motor’s condition before a drone crash, by proposing a concatenated vibration prediction model (CVPM) based on recurrent neural network (RNN) techniques. Subsequently, using the proposed CVPM, [...] Read more.
We analyze the comparative performance of predicting the transition from normal to abnormal vibration states, simulating the motor’s condition before a drone crash, by proposing a concatenated vibration prediction model (CVPM) based on recurrent neural network (RNN) techniques. Subsequently, using the proposed CVPM, the prediction performances of six RNN techniques: long short-term memory (LSTM), attention-LSTM (Attn.-LSTM), bidirectional-LSTM (Bi-LSTM), gate recurrent unit (GRU), attention-GRU (Attn.-GRU), and bidirectional-GRU (Bi-GRU), are analyzed comparatively. In order to assess the prediction accuracy of these RNN techniques in predicting concatenated vibrations, both normal and abnormal vibration data are collected from the motors connected to the drone’s propellers. Consequently, a concatenated vibration dataset is generated by combining 50% of normal vibration data with 50% of abnormal vibration data. This dataset is then used to compare and analyze vibration prediction performance and simulation runtime across the six RNN techniques. The goal of this analysis is to comparatively analyze the performances of the six RNN techniques for vibration prediction. According to the simulation results, it is observed that Attn.-LSTM and Attn.-GRU, incorporating the attention mechanism technique to focus on information highly relevant to the prediction target through unidirectional learning, demonstrate the most promising predictive performance among the six RNN techniques. This implies that employing the attention mechanism enhances the concentration of relevant information, resulting in superior predictive accuracy compared to the other RNN techniques. Full article
(This article belongs to the Special Issue Application of Time Series Analysis and Forecasting in Computer Science)
►▼ Show Figures

Figure 1

19 pages, 3212 KiB  
Article
A Sales Forecasting Model for New-Released and Short-Term Product: A Case Study of Mobile Phones
by Seongbeom Hwang, Goonhu Yoon, Eunjung Baek and Byoung-Ki Jeon
Electronics 2023, 12(15), 3256; https://doi.org/10.3390/electronics12153256 - 28 Jul 2023
Cited by 1 | Viewed by 1779
Abstract
In today’s competitive market, sales forecasting of newly released and short-term products is an important challenge because there is not enough sales data. To address these challenges, we propose a sales forecasting model for new-released and short-term products and study the case of [...] Read more.
In today’s competitive market, sales forecasting of newly released and short-term products is an important challenge because there is not enough sales data. To address these challenges, we propose a sales forecasting model for new-released and short-term products and study the case of mobile phones. The main approach is to develop an integrated sales forecasting model by training the sales patterns and product characteristics of the same product category. In particular, we analyze the performance of the latest 12 machine learning models and propose the best performance model. Machine learning models have been used to compare performance through the development of Ridge, Lasso, Support Vector Machine (SVM), Random Forest, Gradient Boosting Machine (GBM), AdaBoost, LightGBM, XGBoost, CatBoost, Deep Neural Network (DNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM). We apply a dataset consisting of monthly sales data of 38 mobile phones obtained in the Korean market. As a result, the Random Forest model was selected as an excellent model that outperforms other models in terms of prediction accuracy. Our model achieves remarkable results with a mean absolute percentage error (MAPE) of 42.6258, a root mean square error (RMSE) of 8443.3328, and a correlation coefficient of 0.8629. Full article
(This article belongs to the Special Issue Application of Time Series Analysis and Forecasting in Computer Science)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop