COMPARISON OF THE EFFECTIVENESS OF TIME SERIES ANALYSIS METHODS: SMA, WMA, EMA, EWMA, AND KALMAN FILTER FOR DATA ANALYSIS
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,...
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| Format: | Article |
| Language: | English |
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Lublin University of Technology
2023-09-01
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| Series: | Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska |
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| Online Access: | https://ph.pollub.pl/index.php/iapgos/article/view/3652 |
| _version_ | 1797670104151883776 |
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| author | Volodymyr Lotysh Larysa Gumeniuk Pavlo Humeniuk |
| author_facet | Volodymyr Lotysh Larysa Gumeniuk Pavlo Humeniuk |
| author_sort | Volodymyr Lotysh |
| collection | DOAJ |
| description |
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.
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| first_indexed | 2024-03-11T20:54:53Z |
| format | Article |
| id | doaj.art-6d232bc34c3f4d0d9f42637743aad230 |
| institution | Directory Open Access Journal |
| issn | 2083-0157 2391-6761 |
| language | English |
| last_indexed | 2024-03-11T20:54:53Z |
| publishDate | 2023-09-01 |
| publisher | Lublin University of Technology |
| record_format | Article |
| series | Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska |
| spelling | doaj.art-6d232bc34c3f4d0d9f42637743aad2302023-09-30T18:29:56ZengLublin University of TechnologyInformatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska2083-01572391-67612023-09-0113310.35784/iapgos.3652COMPARISON OF THE EFFECTIVENESS OF TIME SERIES ANALYSIS METHODS: SMA, WMA, EMA, EWMA, AND KALMAN FILTER FOR DATA ANALYSISVolodymyr Lotysh0Larysa Gumeniuk1Pavlo Humeniuk2Lutsk National Technical UniversityLutsk National Technical UniversityLutsk National Technical University 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. https://ph.pollub.pl/index.php/iapgos/article/view/3652data analysismodelingmoving averageKalman filter |
| spellingShingle | Volodymyr Lotysh Larysa Gumeniuk Pavlo Humeniuk COMPARISON OF THE EFFECTIVENESS OF TIME SERIES ANALYSIS METHODS: SMA, WMA, EMA, EWMA, AND KALMAN FILTER FOR DATA ANALYSIS Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska data analysis modeling moving average Kalman filter |
| title | COMPARISON OF THE EFFECTIVENESS OF TIME SERIES ANALYSIS METHODS: SMA, WMA, EMA, EWMA, AND KALMAN FILTER FOR DATA ANALYSIS |
| title_full | COMPARISON OF THE EFFECTIVENESS OF TIME SERIES ANALYSIS METHODS: SMA, WMA, EMA, EWMA, AND KALMAN FILTER FOR DATA ANALYSIS |
| title_fullStr | COMPARISON OF THE EFFECTIVENESS OF TIME SERIES ANALYSIS METHODS: SMA, WMA, EMA, EWMA, AND KALMAN FILTER FOR DATA ANALYSIS |
| title_full_unstemmed | COMPARISON OF THE EFFECTIVENESS OF TIME SERIES ANALYSIS METHODS: SMA, WMA, EMA, EWMA, AND KALMAN FILTER FOR DATA ANALYSIS |
| title_short | COMPARISON OF THE EFFECTIVENESS OF TIME SERIES ANALYSIS METHODS: SMA, WMA, EMA, EWMA, AND KALMAN FILTER FOR DATA ANALYSIS |
| title_sort | comparison of the effectiveness of time series analysis methods sma wma ema ewma and kalman filter for data analysis |
| topic | data analysis modeling moving average Kalman filter |
| url | https://ph.pollub.pl/index.php/iapgos/article/view/3652 |
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