Filtreler
Comparison of direct and iterative artificial neural network forecast approaches in multi-periodic time series forecasting

Hamzaçebi C. | Akay D. | Kutay F.

Article | 2009 | Expert Systems with Applications36 ( 2 PART 2 ) , pp.3839 - 3844

Artificial neural network is a valuable tool for time series forecasting. In the case of performing multi-periodic forecasting with artificial neural networks, two methods, namely iterative and direct, can be used. In iterative method, first subsequent period information is predicted through past observations. Afterwards, the estimated value is used as an input; thereby the next period is predicted. The process is carried on until the end of the forecast horizon. In the direct forecast method, successive periods can be predicted all at once. Hence, this method is thought to yield better results as only observed data is utilized in o . . .rder to predict future periods. In this study, forecasting was performed using direct and iterative methods, and results of the methods are compared using grey relational analysis to find the method which gives a better result. © 2008 Elsevier Ltd. All rights reserved Daha fazlası Daha az

Determining of stock investments with grey relational analysis

Hamzaçebi C. | Pekkaya M.

Article | 2011 | Expert Systems with Applications38 ( 8 ) , pp.9186 - 9195

Selecting stock is important problem for investors. Investors can use related financial ratios in stock selection. These kind of worthy financial ratios can be obtained from financial statements. The investors can use these ratios as criteria while they are selecting the stocks. Since dealing with more than one financial ratio, the investing issue becomes multi-criteria decision making (MCDM) problem for the investors. There are various techniques for solving MCDM problems in literature. In this study grey relational analysis (GRA) is used for ordering some financial firms' stocks which are in Financial Sector Index of Istanbul Stoc . . .k Exchange (ISE). Besides, because of the importance of criteria weights in decision making, three different approaches - heuristic, Analytic Hierarchy Process, learning via sample - were experimented to find best values of criteria weights in GRA process. © 2011 Published by Elsevier Ltd Daha fazlası Daha az

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