Open AccessInterval forecasting of time series using orderstatistics
Author(s) -
Valentina Misyura,
M Bogacheva,
E Misyura
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2131/2/022110
Subject(s) - series (stratigraphy) , interval (graph theory) , prediction interval , nonparametric statistics , time series , linear regression , computer science , regression , probabilistic forecasting , nonparametric regression , statistics , mathematics , econometrics , paleontology , combinatorics , probabilistic logic , biology
In the traditional approach of obtaining time series forecasts based on the selected model, the model parameters are first estimated, then a point forecast using the obtained estimatesis made and then an interval forecast with a given probability is made. In the article the authors propose a nonparametric method for obtaining a single-stage interval forecasting of a time series based on constructing predictive and target variables sets using robust statistics and obtaining the forecast boundaries by constructing linear regression models. The predictive algorithm is based on the problems of estimating the parameters of linear multiple regression using a model regularization methods. The results of forecasting prove the expediency and effectiveness of the proposed method.