Forecasting with Univariate Time Series Models: A Case of Export Demand for Peninsular Malaysia’s Moulding and Chipboard

Emang, Diana; Shitan, Mahendran; Ghani, Awang Noor Abd.; Noor, Khamurudin Mohd.

doi:10.5539/jsd.v3n3p157

Cited by 36 publications

(10 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Koutroumanidis et al (2009) used the ARIMA and Artificial Neural Networks (ANN) models as well as a hybrid approach (ARIMA-ANN) to predict the future selling prices of the fuel wood (from broadleaved and coniferous species) produced by Greek state forest farms. Emang et al (2010) used univariate time series models, including Holt-wintersseasonal, ARAR algorithm, and seasonal ARIMA modeling to forecast the future volume of exporting wooden products (including chipboard and moulding) in Malaysia. Mohammadi Limaei et al (2011) analyzed time series and an autoregressive procedure to predict the export and import of wood in Iran.…”

Section: Introductionmentioning

confidence: 99%

Forecasting of Particleboard Consumption in Iran Using Univariate Time Series Models

Tavakkoli¹,

Hemmasi²,

Talaeipour³

et al. 2015

BioResources

View full text Add to dashboard Cite

The performance of the Autoregressive Integrated Moving Average )ARIMA) model and Double and Holt-winters exponential smoothing techniques for forecasting the consumption of particleboard in Iran are compared. Annual time series data from 1978 to 2009 in the modeling process, and observations from 2010 to 2012 were used to check the accuracy of the models' forecasting performance. Also, the models' performances were calculated in terms of RMSE criterion, and the consumption of particleboard in Iran was forecasted up to the year 2017 using the most appropriate model. The results of comparing different forecast models showed that the ARMA (2,1) model yielded the lowest RMSE value compared to the other two models, which makes it more appropriate for the prediction of consumption of particleboard in Iran. Results also revealed that there might be an increasing trend in the consumption of particleboard, i.e., an average annual increasing rate calculated as 5% for particleboard. Thus, it was predicted that the consumption of particleboard would increase from 901,652 m 3 in 2012 to 1,178,320 m 3 in 2017.

show abstract

Section: Introductionmentioning

confidence: 99%

Forecasting of Particleboard Consumption in Iran Using Univariate Time Series Models

Tavakkoli¹,

Hemmasi²,

Talaeipour³

et al. 2015

BioResources

View full text Add to dashboard Cite

show abstract

“…It is worth acknowledging that there have been numerous applications of the autoregressive integrated moving average (ARIMA) on data in several disciplines. [13] estimates and makes some forecast of export demand for moulding and chipboard in Malaysia using univariate time series models like the Holt-Winters Seasonal and the seasonal ARIMA models. [14], conducted a study of earnings but subsequently in [15], ARIMA time series models was also applied to quarterly earnings data of common stockholders for a sample of ninety-four large firms listed on the New York Stock Exchange.…”

Section: Methodsmentioning

confidence: 99%

Time Series Modelling of The Volume of Overland Timber Export in The Bolgatanga Municipality

Kpamma

Bruku

Awaab

2020

JSRR

View full text Add to dashboard Cite

Aims/ Objectives: This research was carried out with the intention of using time series to model the volume of overland timber exported within Bolgatanga municipalityPlace and Duration of Study: Study of the time series was based on a historical data of the volume of timber exported for twenty consecutive years, from 1999 to 2019 within Bolgatanga municipality.Methodology: The three-stage iterative modeling approach for Box Jenkins was used to match an ARIMA model and to forecast both the amount of timber export and the confiscated lumber. ARIMA method incorporates a cycle of autoregressive and a moving average. The three-stage iterative modeling technique of Box Jenkins which were used are model recognition, parameter estimation and/or diagnostic checks were also made. Results: From the preliminary investigation, the study showed that the amount of timber exported in municipality is skewed to the right, suggesting that much of the amount of timber exported is below the average. This, together with the high volatility in the volume of timber exported, indicates that the amount of timber exported within the municipalities during the twenty-year period was low. The plots from the trends also showed robust variations in the volume of timber exported indicating that timber exporters do not have better grips with the concepts and applications of export technology, hence the erratic nature of the volume of timber exported over the period. The quadratic pattern and the ARIMA (1,1,1) model best represented the amount of timber exported.The analysis further indicated that there will be a further decrease in the amount of timber export from the five years projection into the future. Over the last two decades the Bayesian approach to VAR has gained ground. For a future report, this estimation method will be followed to examine the ”long-run equilibrium relationships” between timber export volumes and climate change.Conclusion: The quadratic pattern and the ARIMA (1,1,1) model best represented the amount of timber exported. There will be a further decrease in the amount of timber export from the five years projection into the future.

show abstract

“…Especially, LR approach for SFC showed worst estimation performance. Emang et al [22] gave typical MAPE values for model evaluation. According to these values, MAPE ≤ 10% can be evaluated as high accuracy forecasting model.…”

Section: Performance Comparison Of Modelsmentioning

confidence: 99%

Estimation of operational parameters for a direct injection turbocharged spark ignition engine by using regression analysis and artificial neural network

et al. 2017

View full text Add to dashboard Cite

This study was aimed at estimating the variation of several engine control parameters within the rotational speed-load map, using regression analysis and artificial neural network techniques. Duration of injection, specific fuel consumption, exhaust gas at turbine inlet, and within the catalytic converter brick were chosen as the output parameters for the models, while engine speed and brake mean effective pressure were selected as independent variables for prediction. Measurements were performed on a turbocharged direct injection spark ignition engine fueled with gasoline. A three-layer feed-forward structure and backpropagation algorithm was used for training the artificial neural network. It was concluded that this technique is capable of predicting engine parameters with better accuracy than linear and non-linear regression techniques.

show abstract

Forecasting with Univariate Time Series Models: A Case of Export Demand for Peninsular Malaysia’s Moulding and Chipboard

Cited by 36 publications

References 6 publications

Forecasting of Particleboard Consumption in Iran Using Univariate Time Series Models

Forecasting of Particleboard Consumption in Iran Using Univariate Time Series Models

Time Series Modelling of The Volume of Overland Timber Export in The Bolgatanga Municipality

Estimation of operational parameters for a direct injection turbocharged spark ignition engine by using regression analysis and artificial neural network

Contact Info

Product

Resources

About