Using Artificial Neural Networks for Timeseries Smoothing and Forecasting

Beschreibung

The aim of this publication is to identify and apply suitable methods for analysing and predicting the time series of gold prices, together with acquainting the reader with the history and characteristics of the methods and with the time series issues in general. Both statistical and econometric methods, and especially artificial intelligence methods, are used in the case studies. The publication presents both traditional and innovative methods on the theoretical level, always accompanied by a case study, i.e. their specific use in practice. Furthermore, a comprehensive comparative analysis of the individual methods is provided. The book is intended for readers from the ranks of academic staff, students of universities of economics, but also the scientists and practitioners dealing with the time series prediction. From the point of view of practical application, it could provide useful information for speculators and traders on financial markets, especially the commodity markets.




Gives a survey of artificial neural networks that are suitable for timeseries smoothing and forecasting Offers case studies that can help the users (students, financial experts etc.) to understand the way of using artificial networks, its advantages and disadvantages The results of the case studies are compared with classic statistic methods including the way of calculation, accuracy of results and their limitations

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Jaromír Vrbka

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Artificial Neural Networks Forecasting Timeseries Smoothing Timeseries Statistic Methods

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