Algorithms for Time Series Forecasting and Classification
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".
Deadline for manuscript submissions: 20 September 2024 | Viewed by 2262
Special Issue Editors
Interests: Disaster Safety-Based Data and IoT; Urban Disaster Prevention; Disaster Information Search; Web/App Monitoring
Interests: deep learning; time series forecasting; time series classification; solving job shop scheduling using deep learning; smart factory; smart city; fault diagnosis
Special Issue Information
Dear Colleagues,
Time series data plays an important role in various fields, such as finance, meteorology, biomedicine, smart factories, etc. Time series forecasting (TSF) and classification (TSC) are key tasks aimed at identifying and predicting trends, patterns, and anomalies in these data. This Special Issue is looking for some advanced algorithms for time series forecasting and classification to promote their development and application in related fields. Potential topics include, but are not limited to:
- Advanced algorithms for time series forecasting and classification, including improved traditional algorithms (such as ARIMA or SARIMA), machine learning algorithms (such as support vector machines, decision trees and neural networks) and deep learning algorithms (such as convolution neural networks (CNNs), long-short term memory (LSTM), etc.
- Feature engineering algorithms for time series forecasting and classification, including feature extraction, dimensionality reduction, and selection to improve the accuracy and efficiency of forecasting and classification.
- Case studies of time series forecasting and classification in practical applications, such as financial market forecasting, weather forecasting, stock market analysis, and fault diagnosis.
- Application challenges of time series forecasting and classification algorithms in modeling and representation of time series data with high-noise, small-size time series data.
Prof. Dr. Chang-Soo Kim
Guest Editors
Dr. Xiao Rui Shao
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. Algorithms 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 1600 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 forecasting algorithms
- time series classification algorithms
- ARIMA
- machine learning algorithms
- convolutional neural network
- long-short term memory
- feature extraction algorithms
- noise-reduction algorithms
