scholarly journals Lockdown 2.0 In Malaysia: Evaluating Forecast Performance of Goods Export with Box-Jenkins Methodology and ARIMA Model

2021 ◽  
Vol 3 (2) ◽  
pp. 60-73
Author(s):  
Muhammad Nadzif Ramlan
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Movement Control ◽  
Information Criterion ◽  
Forecast Performance ◽  
Positive Rate ◽  
Moving Average Model ◽  
Control Order ◽  
Performance Rate ◽  
The Empirical Analysis

The purpose of this study is to model the forecast of Malaysia's export of goods using Autoregressive Integrated Moving Average Model (ARIMA) modelling with Box-Jenkins method. The time-series concerned is from the first quarter of 2015 to the first quarter of 2021 based on the Department of Statistics Malaysia (DOSM) data. The empirical analysis focuses on the five criteria for consideration towards the best model: high significant coefficient, high adjusted R-squared value, low sigma squared value, low Akaike Information Criterion (AIC) and low Schwarz Information Criterion (SIC). The study showed that ARIMA (2,1,2) would be the best model to forecast Malaysian export of goods from the second quarter of 2021 to the fourth quarter of 2022. The quarterly forecast opined the performance rate of Malaysian goods export to be at a stable positive rate of 4.9% throughout 2022, indicating the economic recovery progress that Malaysia would acquire from its vaccination programme and Movement Control Order (MCO) done in the previous year. The annual forecast showed a more precise value after comparing the actual and forecast growth value of exports in 2021. This finding is further supported with qualitative analysis about the validity of the forecast values via reports released by sources such as World Bank and Focus Economics.

scholarly journals Implementation of Autoregressive Integrated Moving Average Model to Forecast Raw Material Stock in The Digital Printing Industry

2020 ◽  
Vol 5 (1) ◽  
pp. 13
Author(s):  
Dwi Asa Verano ◽  
Husnawati Husnawati ◽  
Ermatita Ermatita
Keyword(s):  
Raw Materials ◽  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
Raw Material ◽  
Digital Printing ◽  
Printing Industry ◽  
Autoregressive Integrated Moving Average ◽  
Moving Average Model

The technology used in the printing industry is currently growing rapidly. Generally, the digital printing industry uses raw materials in the form of paper production. The use of paper material with large volumes is clear badly in need of purchasing large quantities of paper stock as well. The purchase of paper stocks with a constant amount at the beginning of each month for various types of paper causes a buildup or lack of material stock standard on certain types of paper. During this time the purchase and ordering of raw materials only based on the estimates or predictions of the owner. In this paper proposed forecasting will be carried out in the digital printing industry by applying the ARIMA model for each type of raw material paper with the Palembang F18 digital printing case study. The ARIMA modeling applied will produce different parameters for each materials paper type so as to produce forecasting with the Akaike Information Criterion (AIC) value averages 13.0294%.

scholarly journals Forecasting Malaysia COVID-19 Incidence based on Movement Control Order using ARIMA and Expert Modeler

2020 ◽  
Vol 19 (2) ◽  
Author(s):  
Edre MA ◽  
Muhammad Adil ZA ◽  
Jamalludin AR
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Movement Control ◽  
Time Frame ◽  
Control Measures ◽  
Model Fit ◽  
Quality Of Data ◽  
Auto Regressive ◽  
Control Order ◽  
Time Frames

INTRODUCTION: Coronavirus disease (COVID-19) is a novel pandemic that affects every other country in the world. Various countries have adopted control measures involving restriction of movement. Several studies have used mathematical modelling to predict the dynamic of this pandemic. Forecasting techniques can be used to predict the incidence cases for the short term. The study aims to forecast the COVID-19 incidence using the Auto Regressive Integrated Moving Average (ARIMA) method. MATERIALS AND METHODS: Using publicly available data, we performed a forecast of Malaysia COVID-19 new cases using Expert Modeler Method in SPSS and ARIMA model in R to predict COVID-19 cases in Malaysia. We compare 3 different time frames based on different Movement Control Order (MCO) period. We compare the model fit and prediction across models. RESULTS: All models show static cases for each MCO 7-day prediction. For prediction until 12 May, the third MCO time frame shows the best model fit for both techniques. Both software shows a stationary trend of cases of below 100. CONCLUSION: These MCO models have shown to stabilize the rate of new cases. Further sub analysis and quality of data is needed to improve the accuracy of the model.

scholarly journals Price Analysis and Forecasting for Bitcoin Using Auto Regressive Integrated Moving Average Model

2021 ◽  
Vol 6 (2) ◽  
pp. 47-56
Author(s):  
Olufunke G. Darley ◽  
Abayomi I. O. Yussuff ◽  
Adetokunbo A. Adenowo
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
Test Period ◽  
Suitable Model ◽  
Daily Data ◽  
Moving Average Model ◽  
Time Series Approach ◽  
Short Term Forecasting ◽  
Price Analysis

Abstract This paper investigated Bitcoin daily closing price using time series approach to predict future values for financial managers and investors. Daily data were sourced from CoinDesk, with Bitcoin Price Index (BPI) for 5 years (January 1, 2016 to May 31, 2021) extracted. Data analysis and modelling of price trend using Autoregressive Integrated Moving Average (ARIMA) model was carried out, and a suitable model for forecasting was proposed. Results showed that ARIMA(6,1,12) model was the most suitable based on a combination of number of significant coefficients and values of volatility, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). A two-month test window was used for forecasting and prediction. Results showed a decline in prediction accuracy as number of days of the test period increased; from 99.94% for the first 7 days, to 99.59 % for 14 days and 95.84% for 30 days. For the two-month test period, percentage accuracy was 84.75%. The study confirms that the ARIMA model is a veritable planning tool for financial managers, investors and other stakeholders; especially for short-term forecasting. It is however imperative that the influence of external factors, such as investors’/influencers’ comments and government intervention, that may affect forecasting be taken into consideration.

Prognosticating the spread of covid-19 pandemic based on optimal arima estimators

2020 ◽  
Vol 20 ◽  
Author(s):  
Venuka Sandhir ◽  
Vinod Kumar ◽  
Vikash Kumar
Keyword(s):  
South Africa ◽  
Statistical Analysis ◽  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
Stable Case ◽  
Johns Hopkins University ◽  
Modelling Techniques ◽  
Auto Regressive ◽  
Global Threat

Background: COVID-19 cases have been reported as a global threat and several studies are being conducted using various modelling techniques to evaluate patterns of disease dispersion in the upcoming weeks. Here we propose a simple statistical model that could be used to predict the epidemiological extent of community spread of COVID-19from the explicit data based on optimal ARIMA model estimators. Methods: Raw data was retrieved on confirmed cases of COVID-19 from Johns Hopkins University (https://github.com/CSSEGISandData/COVID-19) and Auto-Regressive Integrated Moving Average (ARIMA) model was fitted based on cumulative daily figures of confirmed cases aggregated globally for ten major countries to predict their incidence trend. Statistical analysis was completed by using R 3.5.3 software. Results: The optimal ARIMA model having the lowest Akaike information criterion (AIC) value for US (0,2,0); Spain (1,2,0); France (0,2,1); Germany (3,2,2); Iran (1,2,1); China (0,2,1); Russia (3,2,1); India (2,2,2); Australia (1,2,0) and South Africa (0,2,2) imparted the nowcasting of trends for the upcoming weeks. These parameters are (p, d, q) where p refers to number of autoregressive terms, d refers to number of times the series has to be differenced before it becomes stationary, and q refers to number of moving average terms. Results obtained from ARIMA model showed significant decrease cases in Australia; stable case for China and rising cases has been observed in other countries. Conclusion: This study tried their best at predicting the possible proliferate of COVID-19, although spreading significantly depends upon the various control and measurement policy taken by each country.

scholarly journals When Moving‐Average Models Meet High‐Frequency Data: Uniform Inference on Volatility

Econometrica ◽  
2021 ◽  
Vol 89 (6) ◽  
pp. 2787-2825 ◽  
Author(s):  
Rui Da ◽  
Dacheng Xiu
Keyword(s):  
High Frequency ◽  
Moving Average ◽  
Information Criterion ◽  
High Frequency Data ◽  
Dependence Structure ◽  
Frequency Data ◽  
Finite Samples ◽  
Moving Average Models ◽  
Moving Average Model ◽  
Itô Semimartingale

We conduct inference on volatility with noisy high‐frequency data. We assume the observed transaction price follows a continuous‐time Itô‐semimartingale, contaminated by a discrete‐time moving‐average noise process associated with the arrival of trades. We estimate volatility, defined as the quadratic variation of the semimartingale, by maximizing the likelihood of a misspecified moving‐average model, with its order selected based on an information criterion. Our inference is uniformly valid over a large class of noise processes whose magnitude and dependence structure vary with sample size. We show that the convergence rate of our estimator dominates n 1/4 as noise vanishes, and is determined by the selected order of noise dependence when noise is sufficiently small. Our implementation guarantees positive estimates in finite samples.

scholarly journals Studying monthly rainfall over Dibrugarh, Assam: Use of SARIMA approach

MAUSAM ◽  
2021 ◽  
Vol 68 (2) ◽  
pp. 349-356
Author(s):  
J. HAZARIKA ◽  
B. PATHAK ◽  
A. N. PATOWARY
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Demand Management ◽  
Arima Model ◽  
Monthly Rainfall ◽  
Series Data ◽  
Data Set ◽  
Modeling And Forecasting ◽  
Moving Average Model

Perceptive the rainfall pattern is tough for the solution of several regional environmental issues of water resources management, with implications for agriculture, climate change, and natural calamity such as floods and droughts. Statistical computing, modeling and forecasting data are key instruments for studying these patterns. The study of time series analysis and forecasting has become a major tool in different applications in hydrology and environmental fields. Among the most effective approaches for analyzing time series data is the ARIMA (Autoregressive Integrated Moving Average) model introduced by Box and Jenkins. In this study, an attempt has been made to use Box-Jenkins methodology to build ARIMA model for monthly rainfall data taken from Dibrugarh for the period of 1980- 2014 with a total of 420 points.  We investigated and found that ARIMA (0, 0, 0) (0, 1, 1)12 model is suitable for the given data set. As such this model can be used to forecast the pattern of monthly rainfall for the upcoming years, which can help the decision makers to establish priorities in terms of agricultural, flood, water demand management etc.  

scholarly journals The Impact of Lengths of Time Series on the Accuracy of the ARIMA Forecasting

2019 ◽  
Vol 4 (3) ◽  
pp. 58
Author(s):  
Lu Qin ◽  
Kyle Shanks ◽  
Glenn Allen Phillips ◽  
Daphne Bernard
Keyword(s):  
Time Series ◽  
Historical Data ◽  
Moving Average ◽  
Arima Model ◽  
Forecasting Accuracy ◽  
Autoregressive Integrated Moving Average ◽  
Enrollment Forecasting ◽  
Moving Average Model ◽  
The Impact ◽  
Practical Impact

The Autoregressive Integrated Moving Average model (ARIMA) is a popular time-series model used to predict future trends in economics, energy markets, and stock markets. It has not been widely applied to enrollment forecasting in higher education. The accuracy of the ARIMA model heavily relies on the length of time series. Researchers and practitioners often utilize the most recent - to -years of historical data to predict future enrollment; however, the accuracy of enrollment projection under different lengths of time series has never been investigated and compared. A simulation and an empirical study were conducted to thoroughly investigate the accuracy of ARIMA forecasting under four different lengths of time series. When the ARIMA model completely captured the historical changing trajectories, it provided the most accurate predictions of student enrollment with 20-years of historical data and had the lowest forecasting accuracy with the shortest time series. The results of this paper contribute as a reference to studies in the enrollment projection and time-series forecasting. It provides a practical impact on enrollment strategies, budges plans, and financial aid policies at colleges and institutions across countries.

scholarly journals SARFIMA model prediction for infectious diseases: application to hemorrhagic fever with renal syndrome and comparing with SARIMA

2020 ◽  
Author(s):  
Chang Qi ◽  
Dandan Zhang ◽  
Yuchen Zhu ◽  
Lili Liu ◽  
Chunyu Li ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Goodness Of Fit ◽  
Moving Average ◽  
Predictive Ability ◽  
Information Criterion ◽  
Hemorrhagic Fever ◽  
Forecast Performance ◽  
Sarima Model ◽  
Control And Prevention ◽  
And Control

Abstract Background The early warning model of infectious diseases plays a key role in prevention and control. Our study aims to using seasonal autoregressive fractionally integrated moving average (SARFIMA) model to predict the incidence of hemorrhagic fever with renal syndrome (HFRS) and comparing with seasonal autoregressive integrated moving average (SARIMA) model to evaluate its prediction effect. Methods Data on notified HFRS cases in Weifang city, Shandong Province were collected from the Disease Reporting Information System of the Shandong Center for Disease Control and Prevention between January 1, 2005 and December 31, 2018. The SARFIMA model considering both the short memory and long memory was performed to fit and predict the HFRS series. Besides, we compared accuracy of fit and prediction between SARFIMA and SARIMA which was used widely in infectious diseases. Results Model assessments indicated that the SARFIMA model has better goodness of fit (SARFIMA(1, 0.11, 2)(1, 0, 1) 12 : Akaike information criterion (AIC): -631.31; SARIMA(1, 0, 2)(1, 1, 1) 12 : AIC: -227.32) and better predictive ability than the SARIMA model (SARFIMA: root mean square error (RMSE): 0.058; SARIMA: RMSE: 0.090). Conclusions The SARFIMA model produces superior forecast performance than the SARIMA model for HFRS. Hence, the SARFIMA model may help to improve the forecast of monthly HFRS incidence based on a long-range dataset.

scholarly journals Forecasting of Paddy Grain and Rice's Price: An ARIMA (Autoregressive Integrated Moving Average) Model Application

2020 ◽  
Vol 14 (2) ◽  
pp. 224
Author(s):  
Fitri Ramadhani ◽  
Ketut Sukiyono ◽  
Melli Suryanty
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Future Price ◽  
International Level ◽  
Price Prediction ◽  
Rice Price ◽  
Stationary Test ◽  
Moving Average Model ◽  
Actual Price

A decision making for a long-term paddy grain and rice price guidelines need a future price prediction and a forecasting model that made based on time progression. The most popular model used is ARIMA. The common problem in forecasting the paddy grain and rice in Indonesia using this model was choosing the best model which fit all type of forecasting. This study aimed to determine the most appropriate ARIMA Model and forecast paddy grain and rice’s price on the farmer level, wholesale level, and international level. The prediction began after the stationary test and the best model selection conducted. The ARIMA model used was chosen by the lowest AIC and SC accuracy value. ARIMA Model used in this study were grain price on the farmer level (1,1,2), grain price on the milling level (1,1,2), rice price on the wholesale level (1,1,3), and rice price on the international level (3,1,7). The rice price prediction in the next sixth months on the farmer level was IDR 5,905.15/kg and the actual price was IDR 5,524.89/kg, on the milling level was IDR 6,014.35/kg and the actual price was IDR 5,641/kg, on the wholesale level was IDR 12,163.92/kg and the actual price IDR 12,115/kg, while the on the international level was US$ 462,065/Ton and the actual price was US$ 408/Ton. This study concluded that the price list at a different level of the market was requiring a different model of ARIMA.

Autoregressive Integrated Moving Average Model to Predict Graduate Unemployment in Indonesia

2017 ◽  
Vol 12 (1) ◽  
pp. 43-50
Author(s):  
Umi Mahmudah
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Series Data ◽  
Average Model ◽  
Autoregressive Integrated Moving Average ◽  
Moving Average Model ◽  
Forecasting Analysis ◽  
Graduate Unemployment ◽  
Point Forecast

AbstractNowadays it is getting harder for higher education graduates in finding a decent job. This study aims to predict the graduate unemployment in Indonesia by using autoregressive integrated moving average (ARIMA) model. A time series data of the graduate unemployment from 2005 to 2016 is analyzed. The results suggest that ARIMA (1,2,0) is the best model for forecasting analysis, where there is a tendency of increasing number for the next ten periods. Furthermore, the average of point forecast for the next 10 periods is about 1,266,179 while its minimum value is 1,012,861 the maximum values is 1,523,156. Overall, ARIMA (1,2,0) provides an adequate forecasting model so that there is no potential for improvement.

Sign in / Sign up

Export Citation Format

Share Document