LENGTHENING THE DATA SERIES BY VALUES OF SIMILAR DATA SERIES SAMPLES
DOI:
https://doi.org/10.24025/2306-4412.3.2021.244266Keywords:
time series, regression, data replenishment, machine learning, sklearn, insufficient data, hydrologyAbstract
The problem of insufficient information essentially influences the choice of approaches and methods of data series analysis, as well as the quality of the obtained results. Considering this problem, the authors believe that the development of such approaches and models for data series lengthen-ing is relevant. The main task of this work is to describe and implement the technology of data series lengthening. The basis for the implementation of the technology is the use of values of similar data series as a signs for the lengthening of a certain data series represented by the same indicators, as well as similar data series. The work describes a scheme for identifying similar data series. According to this scheme, the most similar data series are those that have the smallest distance value and the strongest direct correlation, calculated between the potentially similar series and the series for which the lengthening will take place. For lengthening of the series, the work considers seven models: linear regression; sum of weighted values for a group of similar series; average weighted values for a group of similar series, with a correction to the average value of the series for which the lengthening is performed; random forest; k-nearest neighbors; support vector regression; gradient busting. The calculation experiment was carried out on the series represented by the values of water level indicators rec-orded at hydrological stations located in the water objects of the Dnieper River basin. For the data series of post 79545, located on the river Sluch, Novograd-Volynsky, Zhytomyr region, a lengthening by one year is carried out, i.e. the length of the series increases by 365 values. As a result, it was found that the most similar are the data series of values by the posts 79555 and 79694, which have the lowest values of the calculated distances and the value of the correlation coefficient greater than 0.75. When the series is lengthened, the best results are obtained with the use of two models: the sum of weighted values for a group of similar series and average weighted values for a group of similar series, with a correction to the average value of the series for which the lengthening is performed. In future research it is planned to use the obtained results for the development and analysis of methods for replenishment of missing values in time series.
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