Anomaly Correction in Time Series Data for Improved Forecasting (CROSBI ID 711867)
Prilog sa skupa u zborniku | ostalo | međunarodna recenzija
Podaci o odgovornosti
Ostroski, Dominik ; Slovenec, Karlo ; Brajdic, Ivona ; Mikuc, Miljenko
engleski
Anomaly Correction in Time Series Data for Improved Forecasting
This paper presents a method for detecting and correcting anomalies in time series data. This method was tested on time series data of disk usage over a period of few months. For the method to be able to detect and correct anomalies, it has to calculate the difference of time series, find the mean value of transformed data and use it to set a threshold. Any point in transformed data that has a value higher than the threshold corresponds to an anomaly in original data. After an anomaly is found, data is transformed in such a way that all data before the anomaly is shifted by the value of the anomaly. By removing anomalies this way, trend and seasonality of time series are kept intact. Results show that time series forecasting performed on transformed disk usage time series produces better results than when the original data is used.
time series , anomaly detection , time series fore-casting
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Podaci o prilogu
21073867
2021.
objavljeno
10.23919/contel52528.2021.9495986
Podaci o matičnoj publikaciji
Institute of Electrical and Electronics Engineers (IEEE)
978-9-5318-4271-6
Podaci o skupu
16th International Conference on Telecommunications (ConTEL 2021)
predavanje
30.06.2021-02.07.2021
Zagreb, Hrvatska