Abstract
In the context of the Semantic Web, it may be beneficial for a user to receive a forecast regarding the reliability of an information source. We offer an algorithm for building more effective social networks of trust by using CLRM (classic linear regression models). For managing uncertainty, we introduce some random variables which neither the consumer nor the provider can control its value. Such random variables that can be successively accumulated from each stage of multi-stage forecasts are reduced through the use of analytical tools that combine statistical methods with advances in time series analysis. Time series analysis can relate ’current’ values of a critical variable to its past values and to the values of current. Moreover, to model real world scenario, VAR-GARCH (Vector Auto Regression Generalized Autoregressive Conditional Heteroskedasticity) model is used to represent forecasting results which are generally influenced by interactions between decision makers.
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© 2008 Springer-Verlag Berlin Heidelberg
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Qiu, J., Chen, L., Zhang, Y. (2008). A Prediction Algorithm Based on Time Series Analysis. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5264. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87734-9_71
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DOI: https://doi.org/10.1007/978-3-540-87734-9_71
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87733-2
Online ISBN: 978-3-540-87734-9
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