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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.
Maskurul Alam. 2015. \u201cTime Series Decomposition and Seasonal Adjustment\u201d. Global Journal of Science Frontier Research - F: Mathematics & Decision GJSFR-F Volume 15 (GJSFR Volume 15 Issue F9): .
Crossref Journal DOI 10.17406/GJSFR
Print ISSN 0975-5896
e-ISSN 2249-4626
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Total Score: 104
Country: Bangladesh
Subject: Global Journal of Science Frontier Research - F: Mathematics & Decision
Authors: Maskurul Alam, Matiur Rahman, Sharmin Akter Sumy, Yasin Ali Parh (PhD/Dr. count: 0)
View Count (all-time): 179
Total Views (Real + Logic): 4018
Total Downloads (simulated): 2000
Publish Date: 2015 12, Sat
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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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