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Time Series Decomposition and Seasonal Adjustment

§ Islamic University, Kushtia, Bangladesh

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Time Series Decomposition and Seasonal Adjustment

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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.

References

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Maskurul Alam, Sharmin Akter Sumy, Yasin Ali Parh, Matiur Rahman. 2015. "Time Series Decomposition and Seasonal Adjustment". Global Journal of Science Frontier Research - F: Mathematics & Decision GJSFR-F Volume 15 (GJSFR Volume 15 Issue F9).

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Journal Specifications

Crossref Journal DOI 10.17406/GJSFR

Print ISSN 0975-5896

e-ISSN 2249-4626

Keywords
Classification
GJSFR-F Classification MSC 2010: 49M27
Version of record

v1.2

Issue date
December 12, 2015

Language
English
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Time Series Decomposition and Seasonal Adjustment

Maskurul Alam
Maskurul Alam Islamic University, Kushtia, Bangladesh
Sharmin Sumy
Sharmin Sumy <p>Islamic University, Kushtia, Bangladesh</p>
Yasin Parh
Yasin Parh <p>Islamic University, Kushtia, Bangladesh</p>
Matiur Rahman
Matiur Rahman <p>Islamic University, Kushtia, Bangladesh</p>