Machine Learning Approach For Small Samples ARMA Models Identification | ||||
| المجلة العلمية لقطاع کليات التجارة بجامعة الأزهر | ||||
| Article 5, Volume 24, Issue 1, June 2020, Page 45-66 PDF (1.44 MB) | ||||
| Document Type: المقالة الأصلية | ||||
| DOI: 10.21608/jsfc.2020.248229 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Mohamed Agamy* ; Nader Metwally; Gamal Alshawadfi | ||||
| کلية التجارة بنين - جامعة الأزهر - طريق النصر - أمام قاعة المؤتمرات - مدينة نصر - القاهرة الرقم البريدي / 11751 | ||||
| Abstract | ||||
| This paper proposes an effective machine learning approach to identify small samples data generated from autoregressive moving-average ARMA(p,q) models. The theoretical and practical aspects of the proposed approach are introduced , and its validity was evaluated by the ratio of correct identification(CIR) . For evaluating the validity of the proposed machine learning approach, a simulation study was achieved. 192000 small samples were generated from ARMA(p,q) models with different sample sizes(10,20,30) and different parameters sets through the stationarity and invertibility regions. The ratio of the correct identification is calculated and used for evaluating the proposed approach. The average of CIR for all samples was 99.3% which shows a good performance for the proposed approach. The results also showed that the automatic ARMA identification Is less sensitive to small samples additionally, The proposed approach is quicker , automatic and more accurate alternative. A Python program is written for doing automatic Identification using a machine learning attached in the appendix. | ||||
| Keywords | ||||
| Artificial Intelligence (AI); Machine Learning(ML); Box-Jenkins Identification . The Neural Network Architecture | ||||
| References | ||||
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