基于自动机器学习的时间序列预测模型的建立

Huu-Anh-Duc Cap, Trong-Hop Do, D. Lakew, Sungrae Cho
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引用次数: 0

摘要

时间序列预测是目前一个非常热门的研究领域。轻松查找医学、天气预报、生物学、供应链管理、股票价格预测等领域的各种时间序列数据。随着近年来数据和计算能力的激增,深度学习已成为构建时间序列预测模型的首选方法。传统的机器学习模型——如自回归(AR)、指数平滑(Exponential smoothing)或自回归集成移动平均(ARIMA)——将原始原始数据集手动转换为一组属性,参数的优化也必须基于特征选择,而深度学习模型仅直接从数据中学习特征。因此,它加快了数据准备过程,可以充分学习更复杂的数据模式。本文采用自动机器学习(AutoML)方法设计LSTM深度学习网络,对时间序列数据即心率数据进行预测。该模型的结果可应用于医学和卫生保健领域。
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Building a Time-Series Forecast Model with Automated Machine Learning for Heart Rate Forecasting Problem
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.
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