What are the benefits of deseasonalizing data?
Data deseasonalization is the process of stripping off the seasonal patterns from time-series data when they are being released to public databases (Subanar, 2007). Data deseasonalization is also referred to as seasonal adjustment. For one to get a goodness-of-fit measure that separates the impact of the independent variables, one has to estimate his or her model with deseasonalized values for both the independent and the dependent variables.
Data deseasonalization is very crucial as it provides a more understandable series for analysts predicting the news that is contained in the time series of interests. Secondly, data deseasonalization facilitates the comparison of long-term and the short-term movements among nations and sectors of the economy (Sugiyama and Alexandre, 54). It also plays a vital role in supplying users with the required input for a business cycle analysis and also detecting the turning points as well as decomposition of the trend cycle. Data deseasonalization also helps in applying quality control through both the input and the output orientations that in turn allow for a better comparability with other methods and series.
Why is forecasting important for business?
Business forecasting is the process of predicting the possible future developments in the business in relation to sales, profits, and expenditures (Hanke et al. 1981). Forecasting I very critical in every business as it helps the business in planning as it is very hard to come up with a good business plan without first doing business forecasting by use of industry statistics so as to come up with the best forecast possible.
Business forecasting also helps a business in becoming successful since it contributes a lot to the success of a business. This is because one will make better decisions in the prediction of the future performance of a business if he or she has a better understanding of its historical data (Barker and Joel, 1993). Also, business forecasting will offer better management to the business since there exist external factors that affect a business and the process of forecasting helps a manager to handle the negative effects of the launch or expansion of a business.
Barker, Joel Arthur. Paradigms: The Business of Discovering the Future. New York, NY: HarperBusiness, 1993.
Hanke, John E., and Arthur G. Reitsch. Business Forecasting. Boston: Allyn and Bacon, 1981.
Subanar, Subanar; Mathematics Department, Universitas Gadjah Mada, Yogyakarta, Indonesia, and Suhartono, Suhartono; Statistics Department, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia. THE EFFECT OF DECOMPOSITION METHOD AS DATA PREPROCESSING ON NEURAL NETWORKS MODEL FOR FORECASTING TREND AND SEASONAL TIME SERIES. Institute of Research and Community Outreach - Petra Christian University, 2007. <http://jurnalindustri.petra.ac.id/index.php/ind/article/view/16556>.
Sugiyama, Alexandre Borges. Modeling foreign exchange volatility with intraday data. The University of Arizona, n.d. <http://hdl.handle.net/10150/288791>.
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