Box-Jenkins ARIMA Research Articles

Adults newly infected with hiv in burundi: a box-jenkins arima approach

Using annual time series data on the number of adults (ages 15 and above) newly infected with HIV in Burundi from 1990 – 2018, the study predicts the annual number of adults who will be newly infected with HIV over the period 2019 – 2025. The study applied the Box-Jenkins ARIMA methodology. The diagnostic ADF tests as well as correlogram analysis show that the G series under consideration is an I (2) variable. Based on the AIC, the study presents the ARIMA (0, 2, 1) model as the optimal model. The residual correlogram and the inverse roots of the applied model further reveal that the presented model is stable and suitable for forecasting new HIV infections in adults in Burundi. The results of the study indicate that the number of new HIV infections in adults in Burundi will most likely decline, over the period 2019 – 2023, from approximately 698 to almost 90 new HIV infections. By 2025, Burundi could experience her first zero new HIV infections in adults! This implies that, despite the fact that Vision Burundi 2025 is a highly ambitious blue-print; Vision Burundi 2025 will largely be achieved as far as HIV/AIDS prevention and control is concerned.

Arima Forecasting of ohe prevalence of Anemia in children in Myanmar

Using annual time series data on the prevalence of anemia in children under 5 years of age in Myanmar from 1990 – 2016, the study makes predictions for the period 2017 – 2025. The study applies the Box-Jenkins ARIMA methodology. The diagnostic ADF tests show that, AM, the series under consideration is an I (0) variable. Based on the AIC, the study presents an AR (4) model, which is also called the ARIMA (4, 0, 0) model. This has been found to be the parsimonious model. The diagnostic tests further reveal that the presented model is quite stable and its residuals are not serially correlated. The results of the research indicate that the prevalence of anemia in children in Myanmar will rise from approximately 54.5% in 2017 to almost 64.8% by 2025. This means that anemia is not yet under control in the country. This is a wake up call to both public health policy makers and nutrition specialists in the country.

Arima forecasting of the prevalence of anemia in children in Sierra Leone

Using annual time series data on the prevalence of anemia in children under 5 years of age in Sierra Leone from 1990 – 2016, the study makes predictions for the period 2017 – 2025. The study applies the Box-Jenkins ARIMA methodology. The diagnostic ADF tests show that the AS series under consideration is an I (0) variable. Based on the AIC, the study presents the AR (4) model, also known as the ARIMA (4, 0, 0) model as the optimal model. The diagnostic tests further show us that the presented model is stable and its residuals are not serially correlated. The results of the study indicate that the prevalence of anemia in children in Sierra Leone will rise over the out-of-sample period. By 2025, the country will be having a prevalence of anemia in children of approximately 78.6%.

Arima forecasting of the prevalence of anemia in children in Myanmar

Using annual time series data on the prevalence of anemia in children under 5 years of age in Myanmar from 1990 – 2016, the study makes predictions for the period 2017 – 2025. The study applies the Box-Jenkins ARIMA methodology. The diagnostic ADF tests show that, AM, the series under consideration is an I (0) variable. Based on the AIC, the study presents an AR (4) model, which is also called the ARIMA (4, 0, 0) model. This has been found to be the parsimonious model. The diagnostic tests further reveal that the presented model is quite stable and its residuals are not serially correlated. The results of the research indicate that the prevalence of anemia in children in Myanmar will rise from approximately 54.5% in 2017 to almost 64.8% by 2025. This means that anemia is not yet under control in the country. This is a wake up call to both public health policy makers and nutrition specialists in the country.

Analyzing neonatal deaths in Zimbabwe using box-jenkins arima models

Using annual time series data on neonatal deaths in Zimbabwe from 1966 to 2018, we model and forecast number of neonatal deaths over the next 25 years using the Box – Jenkins ARIMA technique. Diagnostic tests such as the ADF tests show that Neonatal Deaths (ND) series is I (2). Based on the AIC, the study presents the ARIMA (8, 2, 0) model as the optimal model. The diagnostic tests further indicate that the presented model is stable and its residuals are stationary in levels. The results of the study reveal that the numbers of neonatal deaths per year are expected to decline sharply in the next 25 years. In order to keep on reducing neonatal deaths in Zimbabwe, the study offered a four-fold policy prescription.

Forecasting sugarcane production in the Asembagus sugar factory

The production of the sugar industry is increasing from year to year because of the national sugar production and consumption. To achieve the level of production that can meet these needs, the government has requested a national sugar self-sufficiency policy. Estimation of sugarcane production has a big influence on the company's policy considerations to determine further production results. This study aims to predict the production of sugar cane that will be obtained by the Asembagus sugar factory for the next five years. The data used to predict are data from 1979 to 2018 obtained from secondary data from Asembagus factory registration results. The method used is the Box-Jenkins ARIMA method by comparing the measurement of forecasting results with the trend and exponential smoothing methods. The appropriate forecasting result is the ARIMA model (1,1,1) because it has a smaller error value than using other methods. Forecasting results for 2019 are 42055.6 tons in 2020 predicted 43222.9 tons in 2021 predicted to produce 44090.7 tons, in 2022 predicted 44788.2 tons and in 2023 predicted sugarcane production of 45389.2 tons.

PREDICTING DISAGGREGATED TOURIST ARRIVALS IN SIERRA LEONE USING ARIMA MODEL

This study has uniquely made use of Box-Jenkins ARIMA models to address the core of the three objectives set out in view of the focus to add meaningful value to knowledge exploration. The outcome of the research has testified the achievements of this through successful nine months out-of-sample forecasts produced from the program codes, with indicating best model choices from the empirical estimation. In addition, the results also provide description of risks produced from the uncertainty Fan Chart, which clearly provide possible downside and upside risks to tourist visitations in the country. In the conclusion, it was suggested that downside risks to the low-level tourist arrival can be managed through collaboration between authorities concerned with the management of tourism in the country.

The Impact Of Macroeconomic Variables On Real Estate Price Forecasting Modelling In Abuja Nigeria

This paper examined the impact of macroeconomic variables on real estate price forecasting modelling in Abuja, Nigeria using the family of Box-Jenkins ARIMA models. The ARIMA and ARIMAX models were used to forecast real estate residential price in Abuja, Nigeria using quarterly data from 2000Q1 to 2017Q in Naira (N). The outcome revealed that, macroeconomic variables such as consumer price index, price of crude oil, exchange rate of Naira against US dollar, GDP, interest rate, household income has significant positive impact on the real estate residential price forecasting models of 2 bedroom flat, 3 bedroom flat and 5 bedroom flat, while they have strong negative impact on the price forecasting model of 4 bedroom flat. Moreover, ARIMA and ARIMAX provides best out of sample forecasting models.

Selection of Linear Time Series Model on the Basis of Out-of-Sample Prediction Criteria

Background: In linear time series, the in-sample model selection and the out-of-sample model selection are the two common approaches to model selection. However, empirical evidence based on out-of-sample forecast performance is generally considered more trustworthy than evidence based on in-sample performance, which is deficient in providing information about future observations.
 Aim: The aim of the study is to select the best linear time series model suitable to predict Nigeria exchange rates for the period 2002-2018.
 Materials and Methods: Data on naira to pound and naira to euro exchange rates from January 2002 to December 2018, comprising of 204 data points were considered. The data were divided into two portions; the first portion which comprises of 192 observations was used for model building while the second portion with 12 observations was used for out-of-sample prediction evaluation. The Box-Jenkins ARIMA iterative procedure was used in model building while the mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE) and Theil’s U coefficient were the measures of accuracy adopted in selecting the best out-of sample model.
 Results: Our results revealed that, based on in-sample model selection, ARIMA (0, 1, 1) and ARIMA (1, 1, 0) were the appropriate models with minimum information criteria. However, on the basis of out-sample forecast performance evaluation criteria, ARIMA (1, 1, 0) and ARIMA (1, 1, 2) were found to be appropriate out-of-sample models with minimum forecast evaluation criteria. In all, our results showed that, the out-of sample models performed better than their in-sample counterparts in their ability to forecast future values.
 Conclusion: So far, this study revealed that out-of-sample is a better model selection criterion than the in-sample counterpart as evident in its ability to predict future values which is the very essence for modelling in time series.

OPTIMASI PARAMETER MODel AUTOREGRESSIVE MENGGUNAKAN ALGORITMA PARTICLE SWARM OPTIMIZATION

Box-Jenkins ARIMA method is a linear model in time series analysis which is widely used in various fields. One estimation method for Box-Jenkins ARIMA model is OLS method which aims to minimize the number of squared errors. This method is not effective when applied to time series data that is random, nonlinear and non-stationary. In this study discussed the alternative method of the PSO algorithm as an parameter optimization of the ARIMA model. PSO algorithm is an optimization method based on the behavior of a flock of birds or fish. The main advantage of the PSO algorithm is having a simple, easy to implement and efficient concept in calculations. This method is applied to data from PT Perusahaan Gas Negara shares. The results of both methods will be compared. In the AR model (1) the value of MSE is 0.532 and MAPE is 0.993. Meanwhile, the PSO algorithm obtained MSE 0.531 and MAPE 0.988. It was found that the PSO algorithm resulted in smaller MSE and MAPE values and could provide better results.Keywords : Time Series Analysis, Autoregressive, PSO

PERBANDINGAN METODE ARIMA BOX-JENKINS DENGAN ARIMA ENSEMBLE PADA PERAMALAN NILAI IMPOR PROVINSI JAWA TENGAH

Import is activities to enter goods into the territory of a country, both commercial and non-commercial include goods that will be processed domestically. Import is an important requirement for industry in Central Java. The increase in high import values can cause deficit in the trade balance. Appropriate information about the projected amount of imports is needed so that the government can anticipate a high increase in imports through several policies that can be done. The forecasting method that can be used is ARIMA Box-Jenkins. The development of modeling in the field of time series forecasting shows that forecasting accuracy increases if it results from the merging of several models called ensemble ARIMA. The ensemble method used is averaging and stacking. The data used are monthly import value data in Central Java from January 2010 to December 2018. Modeling time series with Box-Jenkins ARIMA produces two significant models, namely ARIMA (2,1,0) and ARIMA (0,1,1). Both models are combined using the ARIMA ensemble averaging and stacking method. The best model chosen from the ARIMA method and ensemble ARIMA based on the least RMSE value is the ARIMA model (2,1,0) with RMSE value of 185,8892 Keywords: Import, ARIMA, ARIMA Ensemble, Stacking, Averaging

ASSESSING THE FORECASTING PERFORMANCE OF ARIMA AND ARIMAX MODELS OF RESIDENTIAL PRICES IN ABUJA NIGERIA

This paper compared the out of sample forecasting ability of two Box-Jenkins ARIMA family models: ARIMAX and ARIMA. The forecasting models were tested to forecast real estate residential price in Abuja, Nigeria with quarterly data of average sales of residential price from the first quarter of year 2000 to the last quarter of year 2017. The result shows that the ARIMAX forecasting models, with macroeconomic factors as exogenous variables such as the household income, interest rate, gross domestic products, exchange rate and crude oil price and their lags, provide the best out of sample forecasting models for 2 bedroom, 3 bedroom, 4 bedroom and 5 bedroom, than ARIMA models. Generally, both ARIMA and ARIMAX models are good for short term forecasting modelling.

Predicting industrial waste disposal management by using Box–Jenkins ARIMA models: Case study of four waste service providers

Predicting industrial waste disposal management by using Box–Jenkins ARIMA models: Case study of four waste service providers

An assessment of the effects of climate change on annual streamflow in rivers in Western Turkey

Global warming and its impacts are known to cause serious problems in sustainability of natural resources. In this study, change-point analysis and trend analysis were applied to climatic (temperature, precipitation, evaporation) and streamflow data for Tuzla, Gediz and Büyük Menderes rivers. Box-Jenkins technique and ARIMA model were used for trend analysis. Results showed that there were decreases in streamflow of all rivers. The paper suggests that climate change effects on streamflow could be changeable and that many factors (anthropogenic effects, geographical location, agricultural activities) should be considered. Management strategies specified regionally are required to mitigate the potential climate change effects.

An Assessment of the Effects of Climate Change on Annual Streamflow in Rivers in Western Turkey

Global warming and its impacts are known to cause serious problems in sustainability of natural resources. In this study, change-point analysis and trend analysis were applied to climatic (temperature, precipitation, evaporation) and streamflow data for Tuzla, Gediz and Buyuk Menderes rivers. Box-Jenkins technique and ARIMA model were used for trend analysis. Results showed that there were decreases in streamflow of all rivers. The paper suggests that climate change effects on streamflow could be changeable and that many factors (anthropogenic effects, geographical location, agricultural activities) should be considered. Management strategies specified regionally are required to mitigate the potential climate change effects.

Comparison between Static and Dynamic Forecast in Autoregressive Integrated Moving Average for Seasonally Adjusted Headline Consumer Price Index

This empirical study has provided interpretive outcome from a univariate forecast using Box-Jenkins ARIMA methodology. The HCPI_SA seasonally adjusted data for Sierra Leone shows a robust model outcome with three months ahead prediction based on the STATIC method result. Test results like Autocorrelation and also comparative values for MAPE and the Inverted Root values have indicated that the model is a good fit. Despite better choice of outcome from the STATIC result in comparison to DYNAMIC forecast, the conclusion a cautious means of advice when using results for policy outcomes and with comparative forecasts highly recommended a way forward in guiding policy makers’ decision.

Continuous self-report engagement responses to the live performance of an atonal, post-serialist solo marimba work

Repetition of musical sections, tempo, and physical sound intensity associate strongly with listeners’ affective responses to tonal music in controlled laboratory studies. What contribution might repetition, temporal and acoustic variables make to observers’ responses where music lacks a diatonic tonal structure, an obvious beat, and is performed live? The present study investigates relationships between musical repetition (motivic cells and phrases), note density (sequentially occurring), and intensity with observers’ continuous self-report ratings of engagement with an unfamiliar, atonal post-serialist solo marimba work performed live. Following training, 19 audience members continuously self-reported engagement on a bi-polar, one-dimensional scale using the portable Audience Response Facility. Note density and intensity contributed significantly (or explained variance) to observer engagement with the performance. Contrary to expectation, the repetition variables did not contribute significantly. Controlling serial correlation using a new modelling approach based on Box–Jenkins ARIMA transfer modelling, density emerged as the prime contributor to observer engagement. A single performance appeared to provide observers with insufficient exposure to understand and respond to the structure of atonal music. Note density potentially enabled observers to segment the musical surface and develop some structural understanding, possibly underpinned by stimulus-driven entrainment processes shaping attentional behaviour and musical expectations.

ModisCfied Holt s Linear Trend Method

Exponential smoothing models are simple, accurate and robust forecasting models and because of these they are widely applied in the literature. Holt's linear trend method is a valuable extension of exponential smoothing that helps deal with trending data. In this study we propose a modified version of Holt's linear trend method that eliminates the initialization issue faced when fitting the original model and simplies the optimization process. The proposed method is compared empirically with the most popular forecasting algorithms based on exponential smoothing and Box-Jenkins ARIMA with respect to its predictive performance on the M3-Competition data set and is shown to outperform its competitors.

Kalman’s Expanding Influence in the Econometrics Discipline

Kalman’s original work was quickly adopted and explored throughout the engineering disciplines after the publishing of his seminal paper in 1960 (Kalman 1960). However, it would be several decades before the utility of his framework was understood and adopted in the econometrics discipline, which had traditionally based its analysis on the Box-Jenkins ARIMA time series models. This paper surveys how Kalman’s work has been applied in developing econometric models and how econometricians have extended and built upon Kalman’s framework to develop new techniques to model complex economic and financial systems over the past 20-25 years.

Modelling and forecasting of tur production in India using ARIMA model

The paper describes an empirical study of modeling and forecasting time series data of tur production in India. Yearly tur production data for the period of 1950-1951 to 2014-2015 of India were analyzed by time-series methods. Autocorrelation and partial autocorrelation functions were calculated for the data. The Box Jenkins ARIMA methodology has been used for forecasting. The diagnostic checking has shown that ARIMA (1, 1, 1) is appropriate. The forecasts from 2015-2016 to 2024-2025 were calculated based on the selected model. The forecasting power of autoregressive integrated moving average model was used to forecast tur production for ten leading years. These forecasts would be helpful for the policy makers to foresee ahead of time the future requirements of tur seed, import and/or export and adopt appropriate measures in this regard.