A Robust Composite Model Approach for Forecasting Malaysian Imports: A Comparative Study

Milad, Mohamed A. H.; Irnawaty, Rose

doi:10.3923/jas.2016.279.285

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…The composite model provides better forecasts than the regression equation or time series model alone because this model provides structural and time series explanations for those parts of the variance that can and cannot be explained structurally, respectively. This result supports the findings in Milad M. et al (2017) Milad M. andRoss (2016).…”

Section: Discussionsupporting

confidence: 92%

“…The results of the (Johansen, 1988) cointegration method show that there is long-run relationship between trade balance and commodity terms of trade, but no long-run relationship between trade balance and income terms of trade in Malaysia. Milad M. et al (2017), Milad M. andRoss (2016) Examined the composite model provides better forecasts than the regression equation or time series model alone. Sanusi et al (2020), developed basic artificial neural network (ANN) models in forecasting the in-sample gross domestic product (GDP) of Malaysia.…”

Section: Literature Reviewmentioning

confidence: 99%

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Composite Model Analysis in Forecasting the Malaysian Imports

AH Milad,

Moh. B. Duzan

2024

IJEFR

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For more than a century, forecasting models have been crucial in a variety of fields. Models can offer the most accurate forecasting outcomes if error terms are normally distributed. Finding out a good statistical model for time series predicting imports in Malaysia is the main target of this study. The decision made during this study mostly addresses Vector Error Correction Method (VECM), composite model (Combined regression-ARIMA), and ARIMA model. The imports of Malaysia from the first quarter of 1991 to the first quarter of 2023 are employed in this study’s quarterly time series data. The forecasting outcomes of the current study demonstrated that the composite model offered more probabilistic data, which improved forecasting the volume of Malaysia’s imports. The (ARIMA) mode, composite model, and VECM model in this study are linear models based on responses to Malaysia’s imports. Future studies might compare the performance of nonlinear and linear models in forecasting.

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Section: Discussionsupporting

confidence: 92%

Section: Literature Reviewmentioning

confidence: 99%

Composite Model Analysis in Forecasting the Malaysian Imports

AH Milad,

Moh. B. Duzan

2024

IJEFR

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“…International Journal of Interpreting Enigma Engineers (IJIEE) result with more accuracy. There are also several research that concentrate on predicting short-term tariff rates using past tariff data [8]. Some writers, for example, used SVMs to predict the tariff rate for a given year based on the tariff rate from previous years [9].Additionally, several writers addressed the distinctions and overlaps between neural networks and statistical techniques in the domain of transportation, specifically in relation to forecasting and tariff rate analysis [10].…”

Section: Original Ar�clementioning

confidence: 99%

Prediction of Tariff Rate Using Multiple Linear Regressionprediction of Tariff Rate Using Multiple Linear Regression

Nandini,

Madhav

2024

IJIEE

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A tariff is a list of costs incurred during the movement of commodities across different distances. Seasonal and non-seasonal variables also affect tariffs. The goal of this study is to use multiple linear regression, a machine learning method, to estimate truck load tariff rates. We will attempt to forecast the truck tariff rates by using a few variables and the machine learning regression procedure previously discussed. By doing this, we can assist the industries in estimating the tariff rates so they can take the appropriate steps and manage and control the cost of transportation to run their businesses profitably

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Untitled

2024

IJEFR

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For more than a century, forecasting models have been crucial in a variety of fields. Models can offer the most accurate forecasting outcomes if error terms are normally distributed. Finding out a good statistical model for time series predicting imports in Malaysia is the main target of this study. The decision made during this study mostly addresses Vector Error Correction Method (VECM), composite model (Combined regression-ARIMA), and ARIMA model. The imports of Malaysia from the first quarter of 1991 to the first quarter of 2023 are employed in this study's quarterly time series data. The forecasting outcomes of the current study demonstrated that the composite model offered more probabilistic data, which improved forecasting the volume of Malaysia's imports. The (ARIMA) mode, composite model, and VECM model in this study are linear models based on responses to Malaysia's imports. Future studies might compare the performance of nonlinear and linear models in forecasting.

show abstract

A Robust Composite Model Approach for Forecasting Malaysian Imports: A Comparative Study

Cited by 5 publications

References 10 publications

Composite Model Analysis in Forecasting the Malaysian Imports

Composite Model Analysis in Forecasting the Malaysian Imports

Prediction of Tariff Rate Using Multiple Linear Regressionprediction of Tariff Rate Using Multiple Linear Regression

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