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
Related Articles
- EP ID EP752187
- DOI 10.47852/bonviewJDSIS3202870
- Views 11
- Downloads 0
3D-STCNN: Spatiotemporal Convolutional Neural Network Based on EEG 3D Features for Detecting Driving Fatigue
Fatigue driving has become one of the main causes of traffic accidents, and driving fatigue detection based on electroencephalogram (EEG) can effectively evaluate the driver's mental state and avoid the occurrence of tra...
A Comparative Analysis of Feature Eliminator Methods to Improve Machine Learning Phishing Detection
This Machine-learning-based phishing detection employs statistical models and algorithms to assess and recognise phishing attacks. These algorithms can learn patterns and features that distinguish between phishing and no...
The Evolving Landscape of Oil and Gas Chemicals: Convergence of Artificial Intelligence and Chemical-Enhanced Oil Recovery in the Energy Transition Toward Sustainable Energy Systems and Net-Zero Emissions
Chemical-enhanced oil recovery (EOR) is a field of study that can gain significantly from artificial intelligence (AI), addressing uncertainties such as mobility control, interfacial tension reduction, wettability altera...
Applications of Quantum Computing in Health Sector
The purpose of this paper is to provide an overview of the current state of quantum computing in the health sector and to explore its potential future applications. Quantum computing has the potential to revolutionize a...
Performance Metrics of an Intrusion Detection System Through Window-Based Deep Learning Models
Intrusion and prevention technologies perform reliably in harsh conditions by fortifying many of the world's highest security sites with few defects in high performance. This paper aims to contribute by designing an intr...