Intra-annual National Statistical Accounts Based on Machine Learning Algorithm
Journal Title: Journal of Data Science and Intelligent Systems - Year 2024, Vol 2, Issue 3
Abstract
The methods used for forecasting financial series are based on the concept that a pattern can be identified in the data and distinguished from randomness by smoothing past values. This smoothing process eliminates randomness from the data, enabling the inherent pattern to be used for forecasting. However, acquiring high-frequency national accounts data can be challenging, and complicated methods are required to achieve disaggregated series that are compatible with annual totals. Therefore, there is a need for simpler techniques to obtain high-frequency data from low-frequency equivalents. Machine learning algorithms are rapidly evolving, and feedforward artificial neural networks (ANNs) with appropriate training mechanisms have been proposed to temporally disaggregate economic series without considering related indicators. This study proposes using the ANNs algorithm to disaggregate national statistical accounts. An application of disaggregating annual Australia gross domestic product data into quarterly data has also been presented. The higher frequency data generated have been compared with the observed quarterly data to assess its accuracy. Comparative study suggests that the ANN-based model outperforms over benchmark methods such as Chow and Lin method (CL1) and Fernandez method (f).
Authors and Affiliations
Sandip Garai, Ranjit Kumar Paul, Mohit Kumar, Anish Choudhury
Keywords
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- EP ID EP752187
- DOI 10.47852/bonviewJDSIS3202870
- Views 22
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