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Paper
Title

Improving Bitcoin Price Prediction Power by Combining Wavelet Approach and Neural Network

Year: 1399
COI: COPSS01_025
Language: EnglishView: 55
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Authors

Maryam Seifaddini - Assistant Professor of Computer Science at University of Guilan, Rasht, Iran;
Amir Habibdoust - Lecturer at University of Guilan, Rasht, Iran;

Abstract:

In this paper, we aim to compare the Bitcoin price forecasting performance of a standard machine learning method by looking at data from two different lenses. Indeed, we combine the standard GMDH-neural network with wavelet analysis. In this regard, we decomposed Bitcoin price over the period 2018/12/27 to 2020/08/26 on daily basis at 6 scales and predict the price based on input at different time-scales spaces. Finally, the data are summed up and transformed into time-domain space. Due to differences between features and price movements of Cryptocurrencies and other financial assets, it would be valuable if traders know about the importance of trade frequencies' impact on forecasting power. The results show a better performance of the wavelet base GMDH-neural network in comparison with the standard method. Moreover, it means that there is valuable information on different scales of Bitcoin prices which are neglected in standard time series.

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Paper COI Code

This Paper COI Code is COPSS01_025. Also You can use the following address to link to this article. This link is permanent and is used as an article registration confirmation in the Civilica reference:

https://civilica.com/doc/1163736/

How to Cite to This Paper:

If you want to refer to this Paper in your research work, you can simply use the following phrase in the resources section:
Seifaddini, Maryam and Habibdoust, Amir,1399,Improving Bitcoin Price Prediction Power by Combining Wavelet Approach and Neural Network,First National Conference on Optimizing Production and Service Systems,Rudsar,https://civilica.com/doc/1163736

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The specifications of the publisher center of this Paper are as follows:
Type of center: دانشگاه دولتی
Paper count: 13,560
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