scholarly journals The Comparison between ARIMA and ARFIMA Model to Forecast Kijang Emas (Gold) Prices in Malaysia using MAE, RMSE and MAPE

2021 ◽  
Vol 6 (3) ◽  
pp. 22-33
Author(s):  
Atiqa Nur Azza Mahmad Azan ◽  
Nur Faizatul Auni Mohd Zulkifly Mototo ◽  
Pauline Jin Wee Mah
Keyword(s):  
Time Series ◽  
Arima Model ◽  
Time Series Models ◽  
Monthly Average ◽  
Arfima Models ◽  
Gold Market ◽  
Univariate Time Series ◽  
The World ◽  
Arfima Model ◽  
Best Fit

Gold is known as the most valuable commodity in the world because it is a universal currency recognized by every single bank across the globe. Thus, many people were interested in investing gold since gold market was always steadier compared to other investment (Khamis and Awang, 2020). However, the credibility of gold was questionable due to the changes in gold prices caused by a variety of circumstances (Henriksen, 2018). Hence, information on the inflation of gold prices were needed to understand the trend in order to plan for the future in accordance with international gold price standards. The aim of this study was to identify the trend of Kijang Emas monthly average prices in Malaysia from the year 2010 to 2021, to determine the best fit time series model for Kijang Emas prices in Malaysia and using univariate time series models to forecast Kijang Emas prices in Malaysia. The ARIMA and ARFIMA models were used in this study to model and forecast the prices of gold (Kijang Emas) in Malaysia. Each of the actual monthly Kijang Emas prices for 2021 were found to be within the 95% predicted intervals for both the ARIMA and ARFIMA models. The performances for each model were checked by considering the values of MAE, RMSE and MAPE. From the findings, all the MAE, RMSE and MAPE values showed that the ARFIMA model emerged as the better model in forecasting the Kijang Emas prices in Malaysia compared to the ARIMA model.

Forecasting of Cotton Production in India using Advanced Time Series Models

2020 ◽  
pp. 583-590
Keyword(s):  
Time Series ◽  
Arima Model ◽  
Data Series ◽  
Time Series Models ◽  
Annual Data ◽  
Cotton Production ◽  
Out Of Sample ◽  
Augmented Dickey Fuller ◽  
Logarithmic Series ◽  
Best Fit

Reliable and timely estimates of cotton production are important providing useful inputs to policymakers for proper foresighted and informed planning. So an attempt was made to forecast the production of cotton at all India level using a time series model. The annual data on production of cotton for the period 1951-52 to 2018-19 was processed. The data were transformed into logarithmic series to stabilize the variance of the series. The stationarity of the data was checked with the help of the Augmented Dickey-Fuller and Phillips-Perron tests. The results of ADF and PP tests confirmed the cotton production series was non-stationary at level, so stationarity in the data was brought by differencing the data series at a first lag. The pattern present in ACF and PACF and results of SCAN and ESACF provided guideline to select the order of non-seasonal ARIMA model. The best fit ARIMA model (ARIMA: 3 1 1) was selected based on AIC criteria and residual diagnostic. The performance of the model was judged based on the MAPE value. The out of sample forecast of cotton production at all India level was carried out for the period 2019-20 to 2021-22. The forecasted values indicated a slight increase in the production of cotton compared to 2018-19.

A COMPARATIVE STUDY BETWEEN UNIVARIATE AND BIVARIATE TIME SERIES MODELS FOR CRUDE PALM OIL INDUSTRY IN PENINSULAR MALAYSIA

2020 ◽  
Vol 5 (1) ◽  
pp. 374
Author(s):  
Pauline Jin Wee Mah ◽  
Nur Nadhirah Nanyan
Keyword(s):  
Time Series ◽  
Palm Oil ◽  
Moving Average ◽  
Forecast Accuracy ◽  
Peninsular Malaysia ◽  
Time Series Models ◽  
Crude Palm Oil ◽  
Univariate Time Series ◽  
Import And Export ◽  
Bivariate Time Series

The main purpose of this study is to compare the performances of univariate and bivariate models on four time series variables of the crude palm oil industry in Peninsular Malaysia. The monthly data for the four variables, which are the crude palm oil production, price, import and export, were obtained from Malaysian Palm Oil Board (MPOB) and Malaysian Palm Oil Council (MPOC). In the first part of this study, univariate time series models, namely, the autoregressive integrated moving average (ARIMA), fractionally integrated autoregressive moving average (ARFIMA) and autoregressive autoregressive (ARAR) algorithm were used for modelling and forecasting purposes. Subsequently, the dependence between any two of the four variables were checked using the residuals’ sample cross correlation functions before modelling the bivariate time series. In order to model the bivariate time series and make prediction, the transfer function models were used. The forecast accuracy criteria used to evaluate the performances of the models were the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The results of the univariate time series showed that the best model for predicting the production was ARIMA  while the ARAR algorithm were the best forecast models for predicting both the import and export of crude palm oil. However, ARIMA  appeared to be the best forecast model for price based on the MAE and MAPE values while ARFIMA  emerged the best model based on the RMSE value.  When considering bivariate time series models, the production was dependent on import while the export was dependent on either price or import. The results showed that the bivariate models had better performance compared to the univariate models for production and export of crude palm oil based on the forecast accuracy criteria used.

scholarly journals Coronavirus (COVID-19) Pandemic in Indonesia: Cases Overview and Daily Data Time Series using Naïve Forecast Method

2021 ◽  
Vol 3 (1) ◽  
pp. 1-8
Author(s):  
Annisa Puspa Kirana ◽  
Adhitya Bhawiyuga
Keyword(s):  
Time Series ◽  
Percentage Error ◽  
Forecast Method ◽  
Monthly Basis ◽  
Average Case ◽  
Monthly Average ◽  
Daily Data ◽  
Time Series Dataset ◽  
The World ◽  
Forecasting Analysis

At the end of December 2019, the virus emerges from Wuhan, China, and resulted in a severe outbreak in many cities in China and expanding globally, including Indonesia. Indonesia is the fourth most populated country globally. As of February 2021, Indonesia in the first rank of positive cases of COVID-19 in Southeast Asia, number 4 in Asia, and number 19 in the world. Our paper aims to provide detailed reporting and analysis of the COVID-19 case overview and forecasting that have hit Indonesia. Our time-series dataset from March 2020 to January 2021. Summary of cases studied included the number of positive cases and deaths due to COVID-19 on a daily or monthly basis. We use time series and forecasting analysis using the Naïve Forecast method.  The prediction is daily case prediction for six months starting from February 1, 2021, to June 30, 2021, using active cases daily COVID-19 data in all provinces in Indonesia. The highest monthly average case prediction is in June, which is 35,662 cases. Our COVID-19 prediction study has a mean absolute percentage error (MAPE) score of 15.85%.

scholarly journals Forward Projections of Turkey's Beekeeping

2019 ◽  
Author(s):  
Yusuf Ziya Tengiz ◽  
Zehra Meliha Tengiz
Keyword(s):  
Time Series ◽  
Human Life ◽  
Arima Model ◽  
Future Trends ◽  
Agricultural Activities ◽  
Honey Production ◽  
The World ◽  
Ancient Times ◽  
Effective Use ◽  
Production Resources

Beekeeping is one of the most common agricultural activities in the world. In addition to the world and human life, it is very important in the pollination and the efficiency of the plants. Beekeeping activities have been done since ancient times as the tradition of Anatolian people. In Turkey grows 75% of the honey plants species determined in the world. This provides a variety of honey in different aroma and flavors. Turkey with 7 796 666 beehives is in 3rd rank, with 114 471 tons of honey production is in 2nd rank and with 4 488 tons of beeswax production in 2017 is in 4th rank in the world bee products have an important role. It is important to determine future trends in developing appropriate policies for our country, which is one of the leading countries in beekeeping activities. The main aims of this study are to forecast honey and beeswax product in Turkey for 2019-2025. Arima model, which is one of the time series analysis, was used in this study. According to the results, it is expected that honey production will increase in these years. However, it is expected the increase in the production of beeswax until 2021. After 2021 year is expected to decrease a little. In our country which has great potential for beekeeping, it will be able to take place among the countries which have a significant influence in foreign trade with the effective use of production resources in the realization of activities.

scholarly journals Forecasting of Economic Indicators (Production, Consumption, Population) of Wheat Crop (A Case Study)

2021 ◽  
Vol 8 (6) ◽  
pp. 38-46
Author(s):  
Mohammad Karim Ahmadzai
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Moving Average ◽  
Arima Model ◽  
Wheat Crop ◽  
Series Analysis ◽  
The People ◽  
Self Sufficiency ◽  
Univariate Time Series ◽  
Short Run

Wheat is the most important food crop in Afghanistan, whether consumed by the bulk of the people or used in various sectors. The problem is that Afghanistan has a significant shortfall of wheat between domestic production and consumption. Thus, the present study looks at the issue of meeting self-sufficiency for the whole population due to wheat shortages. To do so, we employ time series analysis, which can produce a highly exact short-run prediction for a significant quantity of data on the variables in question. The ARIMA models are versatile and widely utilised in univariate time series analysis. The ARIMA model combines three processes: I the auto-regressive (AR) process, (ii) the differencing process, and (iii) the moving average (MA) process. These processes are referred to as primary univariate time series models in statistical literature and are widely employed in various applications. Where predicting future wheat requirements is one of the most important tools that decision-makers may use to assess wheat requirements and then design measures to close the gap between supply and consumption. The present study seeks to forecast Production, Consumption, and Population for the period 2002-2017 and estimate the values of these variables between 2002 and 2017. (2018-2030).  

Forecasting Software Vulnerabilities Using Time-Series Techniques

2019 ◽  
pp. 125-165
Author(s):  
Baidyanath Biswas
Keyword(s):  
Time Series ◽  
Homeland Security ◽  
Moving Average ◽  
Primary Objective ◽  
Data Set ◽  
Software Vulnerabilities ◽  
Systems Security ◽  
Univariate Time Series ◽  
Efficient Prediction ◽  
Best Fit

This chapter discusses the concepts of time-series applications and forecasting in the context of information systems security. The primary objective in such formulation is the training of the models followed by efficient prediction. Although economic and financial forecasting problems extensively use time-series, predicting software vulnerabilities is a novel idea. The chapter also provides appropriate guidelines for the implementation and adaptation of univariate time-series for information security. To achieve this, the authors focus on the following techniques: autoregressive (AR), moving average (MA), autoregressive integrated moving average (ARIMA), and exponential smoothing. The analysis considers a unique data set consisting of the publicly exposed software vulnerabilities, available from the U.S. Dept. of Homeland Security. The problem is presented first, followed by a general framework to identify the problem, estimate the best-fit parameters of that model, and conclude with an illustrative example from the above dataset to familiarize readers with the business problem.

Predicting Daily Confirmed COVID-19 Cases in India

2021 ◽  
pp. 34-51
Author(s):  
Sudip Singh
Keyword(s):  
Time Series ◽  
Root Mean Square Error ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Arima Model ◽  
Family Welfare ◽  
Mean Square ◽  
Univariate Time Series ◽  
Partial Autocorrelation Function ◽  
Auto Regressive

India, with a population of over 1.38 billion, is facing high number of daily COVID-19 confirmed cases. In this chapter, the authors have applied ARIMA model (auto-regressive integrated moving average) to predict daily confirmed COVID-19 cases in India. Detailed univariate time series analysis was conducted on daily confirmed data from 19.03.2020 to 28.07.2020, and the predictions from the model were satisfactory with root mean square error (RSME) of 7,103. Data for this study was obtained from various reliable sources, including the Ministry of Health and Family Welfare (MoHFW) and http://covid19india.org/. The model identified was ARIMA(1,1,1) based on time series decomposition, autocorrelation function (ACF), and partial autocorrelation function (PACF).

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