Building a Time-Series Forecast Model with Automated Machine Learning for Heart Rate Forecasting Problem
- Authors
- Cap, H.-A.-D.; Do, T.-H.; Lakew, D.S.; Cho, Sungrae
- Issue Date
- Oct-2022
- Publisher
- IEEE Computer Society
- Keywords
- automated machine learning; Hear rate; time series forecasting
- Citation
- International Conference on ICT Convergence, v.2022-October, pp 1097 - 1100
- Pages
- 4
- Journal Title
- International Conference on ICT Convergence
- Volume
- 2022-October
- Start Page
- 1097
- End Page
- 1100
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/59788
- DOI
- 10.1109/ICTC55196.2022.9952797
- ISSN
- 2162-1233
- Abstract
- Time series forecasting is currently a very popular field of study. Easily find a variety of time series data in medicine, weather forecasting, biology, supply chain management, stock price forecasting, and more. With the proliferation of data and computing power in recent years, deep learning has become the first choice for building time series predictive models. While traditional Machine Learning models - such as autoregression (AR), Exponential smoothing, or autoregressive integrated moving average (ARIMA) - perform manual conversion of the original raw data set into a set of attributes, and the optimization of the parameter must also be based on feature selection, the Deep Learning model only learns the features directly from the data alone. As a result, it speeds up the data preparation process and can fully learn more complex data patterns. In this paper, we designed LSTM deep learning network using Automated Machine Learning (AutoML) method to predict time series data which is the heart rate data. The results of this model can be applied to the field of medicine and health care. © 2022 IEEE.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - College of Software > School of Computer Science and Engineering > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.