论文标题
时间序列分析和预测的机器学习算法
Machine Learning Algorithms for Time Series Analysis and Forecasting
论文作者
论文摘要
从销售记录到患者的健康进化指标，到处都有时间序列数据。处理这些数据的能力已成为必要，并且时间序列分析和预测也是如此。每个机器学习爱好者都会将这些视为非常重要的工具，因为它们会加深对数据特征的理解。预测用于根据其过去发生的变量预测变量的价值。本文介绍了对用于预测的各种方法的详细调查。从预处理到验证的完整过程也已得到彻底解释。尤其是Arima，Prophet和LSTM，已经考虑了各种统计和深度学习模型。也已经探索和阐明了机器学习模型的混合版本。任何人都可以使用我们的工作来建立对预测过程的良好理解，并确定当今正在使用的各种最先进的模型。
Time series data is being used everywhere, from sales records to patients' health evolution metrics. The ability to deal with this data has become a necessity, and time series analysis and forecasting are used for the same. Every Machine Learning enthusiast would consider these as very important tools, as they deepen the understanding of the characteristics of data. Forecasting is used to predict the value of a variable in the future, based on its past occurrences. A detailed survey of the various methods that are used for forecasting has been presented in this paper. The complete process of forecasting, from preprocessing to validation has also been explained thoroughly. Various statistical and deep learning models have been considered, notably, ARIMA, Prophet and LSTMs. Hybrid versions of Machine Learning models have also been explored and elucidated. Our work can be used by anyone to develop a good understanding of the forecasting process, and to identify various state of the art models which are being used today.
