Improving time series prediction with deep belief network
By: Das, Soumya
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Contributor(s): Nayak, Monalisa.
Publisher: USA Springer 2023Edition: Vol.104(5), Oct.Description: 1103-1118p.Subject(s): Humanities and Applied SciencesOnline resources: Click here
In:
Journal of the institution of engineers (India): Series BSummary: In this paper, the time series data prediction is done using Deep Belief Network (DBN). The time series data chosen are stock price data, exchange rate data, and electricity consumption data. DBN predicts these three datasets. Particle Swarm Optimization and Local Linear Wavelet Neural Network are also used for prediction of these three datasets. The Root Mean Square Error and Mean Absolute Percentage Error parameters are used to validate the performance of the algorithm. DBNs are more efficient than other machine learning algorithms because they generate less error. They are fault tolerant and use parallel processing. They avoid over fitting and increase the model generalization.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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Articles Abstract Database
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School of Engineering & Technology Archieval Section | Not for loan | 2024-0119 |
In this paper, the time series data prediction is done using Deep Belief Network (DBN). The time series data chosen are stock price data, exchange rate data, and electricity consumption data. DBN predicts these three datasets. Particle Swarm Optimization and Local Linear Wavelet Neural Network are also used for prediction of these three datasets. The Root Mean Square Error and Mean Absolute Percentage Error parameters are used to validate the performance of the algorithm. DBNs are more efficient than other machine learning algorithms because they generate less error. They are fault tolerant and use parallel processing. They avoid over fitting and increase the model generalization.
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