COMPARISON OF THE EFFECTIVENESS OF TIME SERIES ANALYSIS METHODS: SMA, WMA, EMA, EWMA, AND KALMAN FILTER FOR DATA ANALYSIS
Volodymyr Lotysh
Lutsk National Technical University (Ukraine)
http://orcid.org/0000-0003-0899-8015
Larysa Gumeniuk
lorapost@gmail.comLutsk National Technical University (Ukraine)
https://orcid.org/0000-0002-7678-7060
Pavlo Humeniuk
Lutsk National Technical University (Ukraine)
http://orcid.org/0000-0002-6251-8548
Abstract
In time series analysis, signal processing, and financial analysis, simple moving average (SMA), weighted moving average (WMA), exponential moving average (EMA), exponential weighted moving average (EWMA), and Kalman filter are widely used methods. Each method has its own strengths and weaknesses, and the choice of method depends on the specific application and data characteristics. It is important for researchers and practitioners to understand the properties and limitations of these methods in order to make informed decisions when analyzing time series data. This study investigates the effectiveness of time series analysis methods using data modeled with a known exponential function with overlaid random noise. This approach allows for control of the underlying trend in the data while introducing the variability characteristic of real-world data. The relationships were written using scripts for the construction of dependencies, and graphical interpretation of the results is provided.
Keywords:
data analysis, modeling, moving average, Kalman filterReferences
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Authors
Volodymyr LotyshLutsk National Technical University Ukraine
http://orcid.org/0000-0003-0899-8015
Authors
Larysa Gumeniuklorapost@gmail.com
Lutsk National Technical University Ukraine
https://orcid.org/0000-0002-7678-7060
Authors
Pavlo HumeniukLutsk National Technical University Ukraine
http://orcid.org/0000-0002-6251-8548
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