Time Series Decomposition and Seasonal Adjustment

Maskurul Alam; Sharmin Akter Sumy; Yasin Ali Parh; Matiur Rahman

Authors

Maskurul Alam
Sharmin Akter Sumy
Yasin Ali Parh
Matiur Rahman

time series decomposition, decomposition models, seasonal adjustment, moving average smoother

Abstract

Many forecasting method are based on some notion that when an underlying pattern exists in a data series. That data can be distinguished from randomness by smoothing (averaging) past values. The effect of this smoothing is to eliminate randomness so the pattern can be projected into the future. It goes without saying that when a data is good enough and have nice pattern then forecast could be done more precisely. One of the main objectives for decomposition is to estimate seasonal effects that can be used to create and present seasonally adjusted values. A seasonally adjusted value removes the seasonal effect from a value so that trends can be seen more clearly. My main aim is to choose a best decomposition method and forecast the data more precisely.

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How to Cite

Time Series Decomposition and Seasonal Adjustment. (2015). Global Journal of Science Frontier Research, 15(F9), 11-19. https://www.journalofscience.org/index.php/GJSFR/article/view/1694

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