Comparison of Methods for Time Series Data Analysis for Further Use of Machine Learning Algorithms

被引：4
|
作者：
Nemethova, Andrea [1 ]
Borkin, Dmitrii [1 ]
Michalconok, German [1 ]
机构：
[1] Slovak Univ Technol Bratislava, Fac Mat Sci & Technol Trnava, Inst Appl Informat Automat & Mechatron, Bratislava, Slovakia
来源：
COMPUTATIONAL STATISTICS AND MATHEMATICAL MODELING METHODS IN INTELLIGENT SYSTEMS, VOL. 2 | 2019年 / 1047卷
关键词：
Data analysis; Forecast; Exponential smoothing;
D O I：
10.1007/978-3-030-31362-3_10
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The aim of this paper is to cover the initial stage of data analysis of time series data. In our research we are working with real world data obtained from a thermal plant. The main objective of this paper is to analyze the data and to discover potential trends. We also present the comparison of various time series data smoothing methods. This stage of research is necessary in order to continue with applying machine learning algorithms in next stage. After brief introduction we introduce the nature and character of given data. In following chapters of this paper we introduce used methods and present the results. The whole data analysis was performed in Phyton.
引用
收藏
页码：90 / 99
页数：10
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