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 23
- Downloads 0
Random Forest Ensemble Machine Learning Model for Early Detection and Prediction of Weight Category
The number of insurgents in our nation today is significantly rising each day, and the majority of those affected are living as internally displaced persons (IDP) in various IDP camps. These people experience a variety o...
Customer Segmentation Using Machine Learning Model: An Application of RFM Analysis
Machine learning (ML) encompasses a diverse array of both supervised and unsupervised techniques that facilitate prediction, classification, and anomaly detection. Among the many fields of application for such techniques...
Voice Biomarkers for Parkinson's Disease Prediction Using Machine Learning Models with Improved Feature Reduction Techniques
As a chronic and life-threatening disease, Parkinson’s disease (PD) causes people to become rigid and inactive and have shaky voices. There is an argument that current PD detection techniques are ineffective due to their...
Fuzzy Logic and Neural Network-based Risk Assessment Model for Import and Export Enterprises: A Review
With the rapid growth in foreign trade business and the continuous expansion of customs functions, the amount of data obtained by customs monitoring systems has drastically increased, and risk management techniques have...
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...