Forecasting natural rubber price in Malaysia using Arima

This paper contains introduction, materials and methods, results and discussions, conclusions and references. Based on the title mentioned, high volatility of the price of natural rubber nowadays will give the significant risk to the producers, traders, consumers, and others parties involved in the production of natural rubber. To help them in making decisions, forecasting is needed to predict the price of natural rubber. The main objective of the research is to forecast the upcoming price of natural rubber by using the reliable statistical method. The data are gathered from Malaysia Rubber Board which the data are from January 2000 until December 2015. In this research, average monthly price of Standard Malaysia Rubber 20 (SMR20) will be forecast by using Box-Jenkins approach. Time series plot is used to determine the pattern of the data. The data have trend pattern which indicates the data is non-stationary data and the data need to be transformed. By using the Box-Jenkins method, the best fit model for the time series data is ARIMA (1, 1, 0) which this model satisfy all the criteria needed. Hence, ARIMA (1, 1, 0) is the best fitted model and the model will be used to forecast the average monthly price of Standard Malaysia Rubber 20 (SMR20) for twelve months ahead

Work design improvement of food steamer at Pau Mira Frozen Food Industry

Pau Mira is a frozen food company located at kilometer 13, Kampung Tanah Merah, Pagoh Muar, Johor. It was devloped by a family in Kampung Tanah Merah. Pau Mira was initially made and sold daily, nearby the road side. Currently, the brand has continued to flourish in the food industry when its entrepreneur Mr. Misnan Ramijo, succeeded in establishing a company to supply increasingly popular frozen food especially its main product which is the frozen ‘pau mira’. The pau is a traditional and local steamed bun made of flour and yeast with various filling inside. It is also called as bao or baozi which is well known in the chinese cuisine. However, the term pau will be further used in this chapter

Forecasting airport passenger traffic: the case of Hong Kong International Airport

Hong Kong International Airport is one of the main gateways to Mainland China and the major aviation hub in Asia. An accurate airport traffic demand forecast allows for short and long-term planning and decision making regarding airport facilities and flight networks. This paper employs the Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) methodology to build and estimate the univariate seasonal ARIMA model and the ARIMX model with explanatory variables for forecasting airport passenger traffic for Hong Kong, and projecting its future growth trend from 2011to 2015. Both fitted models are found to have the lower Mean Absolute Percentage Error (MAPE) figures, and then the models are used to obtain ex-post forecasts with accurate forecasting results. More importantly, both ARIMA models predict a growth in future airport passenger traffic at Hong Kong

Um modelo SARIMA para predição do número de casos de dengue em Campinas, Estado de São Paulo

INTRODUCTION: Forecasting dengue cases in a population by using time-series models can provide useful information that can be used to facilitate the planning of public health interventions. The objective of this article was to develop a forecasting model for dengue incidence in Campinas, southeast Brazil, considering the Box-Jenkins modeling approach. METHODS: The forecasting model for dengue incidence was performed with R software using the seasonal autoregressive integrated moving average (SARIMA) model. We fitted a model based on the reported monthly incidence of dengue from 1998 to 2008, and we validated the model using the data collected between January and December of 2009. RESULTS: SARIMA (2,1,2) (1,1,1)12 was the model with the best fit for data. This model indicated that the number of dengue cases in a given month can be estimated by the number of dengue cases occurring one, two and twelve months prior. The predicted values for 2009 are relatively close to the observed values. CONCLUSIONS: The results of this article indicate that SARIMA models are useful tools for monitoring dengue incidence. We also observe that the SARIMA model is capable of representing with relative precision the number of cases in a next year.INTRODUÇÃO: A predição do número de casos de dengue em uma população utilizando modelos de series temporais pode trazer informações úteis para um melhor planejamento de intervenções públicas de saúde. O objetivo deste artigo é desenvolver um modelo capaz de descrever e predizer a incidência de dengue em Campinas, sudeste do Brasil, considerando a metodologia de Box e Jenkins. MÉTODOS: O modelo seasonal autoregressive integrated moving average (SARIMA) para os dados de incidência de dengue em Campinas, foi implementado no programa R. Ajustamos um modelo baseado na incidência mensal notificada da doença de 1998 a 2008 e validado pelos dados de janeiro a dezembro de 2009. RESULTADOS: O modelo SARIMA (2,1,2) (1,1,1)12 foi o mais adequado aos dados. Este modelo indicou que o número de casos de dengue em um dado mês pode ser estimado pelo número de casos ocorridos há um, dois e doze meses. Os valores preditos para 2009 são relativamente próximos aos valores observados. CONCLUSÕES: Os resultados deste artigo indicam que os modelos SARIMA são ferramentas úteis para o monitoramento da incidência da dengue. Observamos ainda que o modelo SARIMA é capaz de representar com relativa precisão o número de casos de dengue em um ano consecutivo à série de dados usada no ajuste do modelo

Forecasting population changes and service requirements in the regions: a study of two regional councils in Queensland, Australia

Forecasting population growth to meet the service needs of a growing population is a vexed issue. The task of providing essential services becomes even more difficult when future population growth forecasts are unavailable or unreliable. The aim of this paper is to identify the main methods used in population forecasting and thereby select an approach to demonstrate that such forecasting can be undertaken with certainly and transparency, barring exogenous events. We then use the population forecasts to plan for service needs that arise from changes in population in the future. Interestingly, although there are techniques available to forecast such future population changes and much of this forecasting occurs, such work remains somewhat clouded in mystery. We strive to rectify this situation by applying an approach that is verifiable, transparent, and easy to comprehend. For this purpose we select two regional councils in Queensland, Australia. The experience derived from forecasting shows that forecasts for service needs of larger populations are more easily and accurately derived than for smaller populations. Hence, there is some evidence, at least from a service provision point of view, to justify the benefits of council/ municipality amalgamation in recent times in Australia and elsewhere. The methodology used in this paper for population forecasting and the provision of service needs based on such forecasts will be of particular interest to policy decisionmakers and planners.Regional Population forecasting, service provision, Box-Jenkins model

MODELLING TOURISM DEMAND: A COMPARATIVE STUDY BETWEEN ARTIFICIAL NEURAL NETWORKS AND THE BOX-JENKINS METHODOLOGY

This study seeks to investigate and highlight the usefulness of the Artificial Neural Networks (ANN) methodology as an alternative to the Box-Jenkins methodology in analysing tourism demand. To this end, each of the above-mentioned methodologies is centred on the treatment, analysis and modelling of the tourism time series: “Nights Spent in Hotel Accommodation per Month”, recorded in the period from January 1987 to December 2006, since this is one of the variables that best expresses effective demand. The study was undertaken for the North and Centre regions of Portugal. The results showed that the model produced by using the ANN methodology presented satisfactory statistical and adjustment qualities, suggesting that it is suitable for modelling and forecasting the reference series, when compared with the model produced by using the Box?Jenkins methodology.Artificial Neural Networks; ARIMA Models; Time Series Forecasting