{"title":"An enhanced version of the SSA-HJ-biplot for time series with complex structure","authors":"Alberto Silva, Adelaide Freitas","doi":"10.1007/s11634-023-00541-x","DOIUrl":null,"url":null,"abstract":"<div><p>HJ-biplots can be used with singular spectral analysis to visualize and identify patterns in univariate time series. Named SSA-HJ-biplots, these graphs guarantee the simultaneous representation of the trajectory matrix’s rows and columns with maximum quality in the same factorial axes system and allow visualization of the separation of the time series components. Structural changes in the time series can make it challenging to visualize the components’ separation and lead to erroneous conclusions. This paper discusses an improved version of the SSA-HJ-biplot capable of handling this type of complexity. After separating the series’ signal and identifying points where structural changes occurred using multivariate techniques, the SSA-HJ-biplot is applied separately to the series’ homogeneous intervals, which is why some improvement in the visualization of the components’ separation is intended.</p></div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"18 2","pages":"409 - 430"},"PeriodicalIF":1.4000,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Data Analysis and Classification","FirstCategoryId":"94","ListUrlMain":"https://link.springer.com/article/10.1007/s11634-023-00541-x","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
引用次数: 0
Abstract
HJ-biplots can be used with singular spectral analysis to visualize and identify patterns in univariate time series. Named SSA-HJ-biplots, these graphs guarantee the simultaneous representation of the trajectory matrix’s rows and columns with maximum quality in the same factorial axes system and allow visualization of the separation of the time series components. Structural changes in the time series can make it challenging to visualize the components’ separation and lead to erroneous conclusions. This paper discusses an improved version of the SSA-HJ-biplot capable of handling this type of complexity. After separating the series’ signal and identifying points where structural changes occurred using multivariate techniques, the SSA-HJ-biplot is applied separately to the series’ homogeneous intervals, which is why some improvement in the visualization of the components’ separation is intended.
期刊介绍：
The international journal Advances in Data Analysis and Classification (ADAC) is designed as a forum for high standard publications on research and applications concerning the extraction of knowable aspects from many types of data. It publishes articles on such topics as structural, quantitative, or statistical approaches for the analysis of data; advances in classification, clustering, and pattern recognition methods; strategies for modeling complex data and mining large data sets; methods for the extraction of knowledge from data, and applications of advanced methods in specific domains of practice. Articles illustrate how new domain-specific knowledge can be made available from data by skillful use of data analysis methods. The journal also publishes survey papers that outline, and illuminate the basic ideas and techniques of special approaches.
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