ARIMA model and forecasting with three types of pulse prices in Bangladesh: a case study

Hossain, Zakir; Samad, Q. A.; Ali, Zulficar

doi:10.1108/03068290610651652

Cited by 20 publications

(13 citation statements)

References 3 publications

(2 reference statements)

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“…Y is the forecast value in time t and a t Y is the actual value in time t. Hossain et al (2006) used the Mean Percent Forecast Error (MPFE) which is defined as:…”

Section: Root Mean Square Error Percentage (Rmspe)mentioning

confidence: 99%

Jute Production in Bangladesh: A Time Series Analysis

Abdulla¹

2015

Journal of Mathematics and Statistics

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Jute is called the Golden Fiber of Bangladesh. Bangladesh is currently the second largest producer of jute fiber. Bangladesh falls behind its other competitors in applying recent technological advancements. In terms of world export of jute fiber, Bangladesh's share is more than 70%, which makes Bangladesh the largest exporter of jute fiber in the world. The global demand for jute and allied products has seen a steady increase driven by a fresh comeback for biodegradable fiber as people now look for eco-friendly products replacing synthetics. The main purpose of this research is to identify the Auto-Regressive Integrated Moving Average (ARIMA) model that could be used to forecast the production of jute in Bangladesh. This study considered the published secondary data of yearly jute production in Bangladesh over the period 1972 to 2013. The best selected Box-Jenkin's ARIMA model for forecasting the jute productions in Bangladesh is ARIMA(1,1,1). The comparison between the original series and forecasted series shows the same manner which indicate the fitted model behaved statistically well and suitable to forecast the jute productions in Bangladesh i.e., the models forecast well during and beyond the estimation period.

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“…Y is the forecast value in time t and a t Y is the actual value in time t. Hossain et al (2006) used the Mean Percent Forecast Error (MPFE) which is defined as:…”

Section: Root Mean Square Error Percentage (Rmspe)mentioning

confidence: 99%

Jute Production in Bangladesh: A Time Series Analysis

Abdulla¹

2015

Journal of Mathematics and Statistics

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“…Model prediction mainly reveals the development law of event sequence from the angle of sequence self-correlation. In recent years, models have been widely used in energy, economy, electricity, construction, transportation, health and other fields for the scientific and reliable prediction of resources in various industries to provide the possibility [32][33].…”

Section: Methodsmentioning

confidence: 99%

Forecast and Analysis of China's Health Human Resources and Medical Higher Education Development Demand

Liu

et al. 2019

Preprint

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Background: Statistics show that as of the end of December 2017, there were 11.749 million health workers and 898.82 million health technicians in China. In recent years, with the rapid development of the economy, the rapid expansion of the health manpower, which requires more scientific and rational health manpower forecasting, health manpower planning.It is important to strengthen and study the forecast of the future demand of the health workforce.Methods: On the basis of analyzing the current situation, changing trend and problems of China's human resources allocation and higher medical education, this study uses ARIMA model to forecast the demand of health workers in China from 2018 to 2024, and compares the difference smaller supply and demand of China's total health manpower.To determine the future development trend of China's health manpower, and finally explore the scientific and reasonable forecasting method of human resources for health. Results: China's total health manpower is relatively adequate, the proportion of medical care is unreasonable, medical staff is inadequate. The ARIMA model predicts an improvement in the inversion of health care in China and a continued increase in the demand for health care.By 2024, China's demand for health personnel will reach 17.4563 million, but the health manpower supply is still not keeping pace with the growth rate of demand, and the quantitative gap is still gradually widening in the short term, the gap between supply and demand in 2024 reached 1.4859 million people, medical and health institutions and medical education institutions are under greater pressure. Conclusion: In view of the forecast results of China's health human resources in this study, policy makers and health managers should pay more attention to and support the forecastplanning of health human resources, strengthen the government's macro-control, and actively solve the shortage of health manpower. Actively explore scientific models or methods for forecasting the demand of health workers, guide the planning practice of health manpower, guide the training of health personnel in colleges and universities, moderately expand the scale of medical education and training, and vigorously improve the quality of medical education.

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“…Prasad et al [63] applied ARIMA model to the prediction of India's total export value. Hossain et al [64] used ARIMA to forecast the prices of motor, mash and mung. For energy, Edigera et al [65] used ARIMA to forecast Turkey's primary energy consumption and found that the ARIMA forecasting of the total primary energy demand appears to be more reliable than the summation of the individual forecasts.…”

Section: Review Of Arima Modelmentioning

confidence: 99%

Comparison of Forecasting Energy Consumption in East Africa Using the MGM, NMGM, MGM-ARIMA, and NMGM-ARIMA Model

Han

2019

Energies

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Forecasting energy demand is the basis for sustainable energy development. In recent years, the new discovery of East Africa’s energy has completely reversed the energy shortage, having turned the attention of the world to the East African region. Systematic research on energy forecasting in Africa, particularly in East Africa, is still relatively rare. In view of this, this study uses a variety of methods to comprehensively predict energy consumption in East Africa. Based on the traditional grey model, this study: (1) Integrated the power coefficient and metabolic principles, and then proposed non-linear metabolic grey model (NMGM) forecasting model; (2) Used Auto Regressive Integrated Moving Average Model (ARIMA) for secondary modeling, and then developed a metabolic grey model-Auto Regressive Integrated Moving Average Model (MGM-ARIMA) and non-linear metabolic grey model-Auto Regressive Integrated Moving Average Model (NMGM-ARIMA) combined models. In terms of the prediction interval, the data for 2000–2017 is a fit to the past stage, while the data for 2018–2030 is used for the prediction of the future stage. To measure the effect of the prediction, the study used the average relative error indicator to evaluate the accuracy of different models. The results indicate that: (1) Mean relative errors of NMGM, MGM-ARIMA, and NMGM-ARIMA are 2.9697%, 2.0969%, and 1.4654%, proving that each prediction model is accurate; (2) Compared with the single model, the combined model has higher precision, confirming the superiority and feasibility of model combination. After prediction, the conclusion shows that East Africa’s primary energy consumption will grow by about 4 percent between 2018 and 2030. In addition, the limitation of this study is that only single variable are considered.

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ARIMA model and forecasting with three types of pulse prices in Bangladesh: a case study

Cited by 20 publications

References 3 publications

Jute Production in Bangladesh: A Time Series Analysis

Jute Production in Bangladesh: A Time Series Analysis

Forecast and Analysis of China's Health Human Resources and Medical Higher Education Development Demand

Comparison of Forecasting Energy Consumption in East Africa Using the MGM, NMGM, MGM-ARIMA, and NMGM-ARIMA Model

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