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All Journal International Journal of Electrical and Computer Engineering Palmyra Fiber as Additional Materials on Solid Concrete Brick of Aggregate
Ahmad, Nawaz
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Materials Science & Nanotechnology

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Forecast Foreign Exchange Rate: The Case Study of PKR/USD Asadullah, Muhammad; Ahmad, Nawaz; Dos-Santos, Maria José Palma Lampreia
Mediterranean Journal of Social Sciences Vol. 11 No. 4 (2020): July 2020
Publisher : Richtmann Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36941/mjss-2020-0048

Abstract

The main aim of this paper is to forecast the future values of the exchange rate of the USD. Dollar (USD) and Pakistani Rupee (PR). For this purpose was used the ARIMA model to forecast the future exchange rates, because the time series was stationary at first difference. Data reported to five years ranging from the first day of April 2014 to 31st March 2019. The results proved that ARIMA (1,1,9) is the most suitable model to forecast the exchange rate. The difference between the forecasted values and actual values are less than 1%; therefore, it was found that the ARIMA is robust and this model will be helpful for the government functionaries, monetary policymakers, economists and other stakeholders to identify and forecast the future trend of the exchange rate and make their policies accordingly.
Evaluation of machine learning approach in modelling and forecasting real gross domestic product growth: a comparative study Qureshi, Moiz; Ismail, Muhammad; Ahmad, Nawaz; Hussain, Ibrar; Ghoto, Abbas Ali; Vveinhardt, Jolita
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1339-1349

Abstract

This study aims to provide an efficient and accurate machine-learning approach for modelling and forecasting the real gross domestic production (GDP) in the context of Pakistan. The study forecasts Pakistan's GDP growth rate using different forecasting models, such as naïve, seasonal naïve (SNaive), smoothing, and k-nearest neighbors (k-NN). Machine learning algorithms provide additional advice for data-driven decision-making. According to the findings, the k-NN-based forecasting gives minimum mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE) compared to the other three models. Economic policymakers can use accurate models to measure significant economic activity and formulate plans. The results indicate that the model produced accurate projections of future GDP levels for Pakistan.
Co-Authors Asadullah, Muhammad Dos-Santos, Maria José Palma Lampreia Ghoto, Abbas Ali Hussain, Ibrar Muhammad Ismail Qureshi, Moiz Vveinhardt, Jolita