Moving Average Method Research Articles

Peramalan Jumlah Penggunaan Kuota Internet Menggunakan Metode Autoregressive Integrated Moving Average (ARIMA)

Internet quota is the number of limits or limits of usage in internet use.To overcome this, it is necessary to forecast the amount of internet quota usage. The purpose of this study is to predict the amount of internet quota usage using the Autoregressive Integrated Moving Average (ARIMA) method. The ARIMA method commonly called the Box-Jenkins method is a method used for short-term forecasting with the assumption that the time series data used must be stationary, meaning that the average variation of the data in question is constant. The bedt model obtained to predict the amount of internet quota usage is the AR (1) or ARIMA (1,0,0) models. From the forecasting results it can be seen that the amount of internet quota usage is increasing every day

Analisis Komparatif ARIMA dan Prophet dengan Studi Kasus Dataset Pendaftaran Mahasiswa Baru

This research presents all studies, methodologies, and results about testing the accuracy of predictions on new student marketing data by region using the Prophet and Autoregressive Integrated Moving Average (ARIMA) methods. The dataset selected for this study uses 26 years of actual data that has an annual interval. The data was prepared for time series forecasting analysis, therefore, several numbers of data preprocessing were applied such as log transformation and resampling. To get efficient variables, the best variables will be sought to improve the accuracy of predictions. Both models will conduct training and test data to produce values that can be compared using the metric regression model. Based on the training conducted, Prophet has better performance than ARIMA.

Development of wind energy market in the European Union

Renewable energy sources (RES) can play a significant role in economic growth. This article examines the development of the wind energy market in the EU. The applicable regulations, directives, and legal acts are reviewed, and the State’s role in the development of the renewable energy market is discussed. The analyses were carried out based on the available literature and Eurostat data. The authors of the paper used basic statistics to summarize the changes in wind energy and make a prognosis. The data analysis uses the Autoregressive Integrated Moving Average Method (ARIMA). The survey found that the highest average wind cumulative installed capacities were in Germany (31026 MW), Spain (18602 MW), United Kingdom (7894 MW) and France (6108 MW). Our analysis indicates that the biggest average annual installed wind capacity was in Germany (2952.6 MW), Spain (1224.5 MW) and the United Kingdom (1363.8 MW). These same countries achieved the highest average wind gross electricity production in the years 2004–17: Germany (50.9 TWh), Spain (39.3 TWh) and United Kingdom (18.9 TWh). The study finds that wind cumulative installed capacity will increase in most countries of the EU. • The European Union onshore wind energy market seems to be stable. • Wind power plays an important role as a source of energy supply. • The most important wind gross electricity producers in the EU are: Germany and Spain. • The highest increase of wind cumulative installed capacity in 2022 will be in Croatia.

Comparison of Rice Price Forecasting Using the ARIMA Method on Amazon Forecast and Sagemaker

Rice is one of the main commodities of trade in Indonesia. PT Food Station as the management company of Cipinang Rice Main Market every day publishes data on price, type of rice and the amount of rice that enters and exits Jakarta area. This study aims to forecast rice prices in the Jakarta area using data held by PT FoodStation during the 2016-2018 data period. Rice price prediction is carried out for the next 30 days using the Auto Regressive Integrated Moving Average (ARIMA) method on the Amazon Forecast and Amazon Sagemaker platforms. The ARIMA model is a form of regression analysis that measures the strength of one dependent variable that is relatively influential on other change variables. The ARIMA model is a special type of regression model in which the dependent variable is considered stationary and the independent variable is the lag or previous value of the dependent variable itself and the error lag. ARIMA is a combination of auto-regressive and moving average processes. The final result obtained in this experiment is that the ARIMA model on Amazon Sagemaker cloud computing is superior when compared to Amazon Forecast. From the experimental results obtained the results of Amazon Sagemaker RMSE (313.379941) are smaller than Amazon Forecast (322.4118029). So it can be concluded that the ARIMA model run at Amazon Sagemaker is more accurate than Amazon Forecast for forecasting the price of rice for 30 days at the Cipinang Rice Main Market

Performance of Genetic Algorithm-Support Vector Machine (GA-SVM) and Autoregressive Integrated Moving Average (ARIMA) in Electric Load Forecasting

The main business focus of an electric power service provider is to meet the consumers’ demand in time and quality as required. The increase of electric load demand is influenced by various factors, for example the development of technology, business, region, standard of life, climatic and weather changes, or even consumers behavior. They must be considered by the power service provider in order to anticipate the load increase beyond the company’s capability and the existing power generator capacity. This study focuses on comparing the performances of two methods in electric load demand forecasting. The Genetic Algorithm-Support Vector Machine (GA-SVM) and the Autoregressive Integrated Moving Average (ARIMA) methods are applied for the prediction of daily load in Malang city, Indonesia, which is under the service coverage of the Indonesian national electricity provider, PT PLN Sub Unit P3B Jawa Timur-Bali. Two specific influencing factors, temperature and precipitation, are considered. The performance comparison is based on the error parameters of Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results of study indicate that the use of GA-SVM method provides better performance than that of the ARIMA method.

PENERAPAN METODE SINGLE MOVING AVERAGE DAN EXSPONENTIAL SMOOTHING PADA USAHA ASRIE MODESTA

This study aims to (1) analyze the number of demands for batik products in the second period of 2018. (2) To analyze the most appropriate forecasting method. (3) To analyze the forecasting of the first period in 2019 using the selected forecasting method.
 This reseach uses primary data and secondary data with data collection techniques using interviews, observation, and documentation. The analysis used is Single Moving Averages and Exsponential Smoothing. 
 The results of research in forecasting demand for batik products in 2019 with the Single Moving Average method are 3,936 units with Mean Absolute Deviation (MAD) of 632.5 units and Mean Square Error (MSE) of 693,718 units. And the Exsponential Smoothing Alpha 0.05 method is 2,788,879 units, with Mean Absolute Deviation (MAD) of 694,318 units and Mean Square Error (MSE) of 960,665 units. The method suggested to company in making forecast predictions is to use the Single Moving Averages method because it has the smallest error rate that compared to the Exsponential Smoothing method with an Alpha value of 0.05.

Precipitation Sequence Analysis of Representative Stations in Shandong Province from 1956 to 2016

Based on the monthly precipitation observation data of 17 hydrological representative stations in Shandong Province from 1956 to 2016, Moving-average method, Mann-Kendall test method, precipitation concentration degree and concentration period were used to comprehensively study the distribution characteristics of precipitation in Shandong. The results show that in terms of inter-annual changes, the annual precipitation in Shandong province shows a declining trend, and the precipitation in coastal areas is generally larger than that in inland areas. Judging from the annual distribution, the precipitation in Shandong during the flood season shows a downward trend, and the non-flood season precipitation shows an upward trend. From 1956 to 2016, the precipitation concentration degree and the precipitation concentration period in Shandong Province showed a weak decline.

A Causal Long Short-Term Memory Sequence to Sequence Model for TEC Prediction Using GNSS Observations

The necessity of predicting the spatio-temporal phenomenon of ionospheric variability is closely related to the requirement of many users to be able to obtain high accuracy positioning with low cost equipment. The Precise Point Positioning (PPP) technique is highly accepted by the scientific community as a means for providing high level of position accuracy from a single receiver. However, its main drawback is the long convergence time to achieve centimeter-level accuracy in positioning. Hereby, we propose a deep learning-based approach for ionospheric modeling. This method exploits the advantages of Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNN) for timeseries modeling and predicts the total electron content per satellite from a specific station by making use of a causal, supervised deep learning method. The scope of the proposed method is to compare and evaluate the between-satellites ionospheric delay estimation, and to aggregate the Total Electron Content (TEC) outcomes per-satellite into a single solution over the station, thus constructing regional TEC models, in an attempt to replace Global Ionospheric Maps (GIM) data. The evaluation of our proposed recurrent method for the prediction of vertical total electron content (VTEC) values is compared against the traditional Autoregressive (AR) and the Autoregressive Moving Average (ARMA) methods, per satellite. The proposed model achieves error lower than 1.5 TECU which is slightly better than the accuracy of the current GIM products which is currently about 2.0–3.0 TECU.

Comparison of exponential smoothing method and autoregressive integrated moving average (ARIMA) method in predicting dengue fever cases in the city of Palembang

Forecasting is the process of making a statement about an event where the event has not been known or observed. For pharmaceutical students, learning about forecasting techniques can help determine the treatment of various diseases, one of which is dengue fever. Dengue fever is an acute disease caused by the dengue virus. Dengue fever is still a public health issue in major cities in Indonesia, one of which is Palembang. Based on the profile done by Palembang City’s Public Health Office in 2017, dengue fever cases in the area from year to year tend to fluctuates. To get the overview of the number of dengue fever cases in the upcoming years, time series forecasting methods are used, namely the Exponential Smoothing method and the Autoregressive Integrated Moving Average (ARIMA) method. Afterward, the results of predictions from the two methods are compared. Forecasting using the ARIMA method gives the smallest MSE and MAE results of 108077.877 and 172.424, respectively, compared to the Exponential Smoothing method. This means that the ARIMA method is better at predicting the number of dengue fever cases in Palembang in the coming years.

Modeling and Predicting of Motor Insurance Claim Amount using Artificial Neural Network

In India the insurance industry is in its growth stage. It consists of 58 insurance companies of which 24 in life and 34 are non-life insurance. The Non-life Insurance companies which cater to motor insurance business presently utilize different trend models to forecast paid claim amount. Motor Insurance Claim amount prediction is one of the most difficult tasks to accomplish in financial forecasting due to the complex nature of data points. The main objective of this study is to determine a reliable time series forecasting model to predict own damage (OD) claim amount of motor insurance data in India from 1981 to 2016. In this context, the annual time series claim data was collected and modeled by using the Generalized linear model (GLM), Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) method. The validation of the model has been done by comparison of predicted and actual values for the period of 36 years. Also, different types of possible models were evaluated using Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE) for accuracy. The results showed that ANN outperformed other traditional time series models (GLM & ARIMA) for predicting the future own damage claim amount with a lesser residual error. Further, the outcome of these data analytics studies would help Insurance companies to have an idea about the expected future claim amounts with more accuracy. Thus, predicting the Motor insurance's own damage claim will help insurance companies to budget their future revenue.

A dinâmica da taxa de câmbio em países emergentes: uma perspectiva Pós-Keynesiana dos casos do Brasil e do México entre 2000 e 2017

Na perspectiva pós-keynesiana, a taxa de câmbio é considerada um tipo de ativo financeiro sujeito às decisões de alocação de portfólio dos agentes que atuam em um sistema monetário e financeiro internacional hierárquico e assimétrico. Baseado nesta perspectiva, este artigo realiza um estudo de caso comparativo das taxas de câmbio do Brasil e do México nos anos 2000, destacando a influência de fatores institucionais. O estudo cobre seis fases do ciclo financeiro internacional e utiliza o Método de Média Móvel Exponencial e o coeficiente de Pearson para analisar a relação entre as taxas de câmbio e os determinantes da equação keynesiana. Os resultados mostram que, embora as taxas de câmbio estudadas tenham um co-movimento semelhante, existem diferenças importantes em relação à sua volatilidade e ao impacto desses determinantes.

Short-term wind speed forecasting framework based on stacked denoising auto-encoders with rough ANN

In this paper, a multi-modal short-term wind speed prediction framework has been proposed based on Artificial Neural Networks (ANNs). Given the stochastic behavior and high uncertainty of wind speed, in modern power systems with high penetration of wind power, precise wind speed forecasting is necessary for the power utility managers. To ensure that the prediction results are sufficiently precise, in this study, a multi-modal method is designed based on denoising and prediction modules. In denoising module, at first, the optimal configuration of Stacked Denoising Auto-encoders (SDAEs) is determined based on Genetic Algorithm (GA), and, then, SDAEs are applied for pre-training the data to reduce the input data noise. Due to the fact that different data sources have different noise bases, the optimal structure of the denoising module is arranged completely independent of various input data. In the forecasting module, the Sinusoidal Rough-Neural Network (SR-NN) is utilized to predict wind speed. After that, denoising and forecasting modules are stacked together to make the holistic deep structure network. In this study, the Deep Learning (DL) approach has been utilized to extract more robust features from the input data in the denoising process. To handle the high intermittent behavior of wind speed, the rough neurons are considered. Rough neurons are made up of two pairs of conventional neurons that are known to be upper and lower bound neurons. Since the wind speed has a periodic and nonlinear behavior, an ANN, based on the sinusoidal activation function, is more accurate than the typical type of sigmoid activation function. In this paper, weather data from Ahar, Iran, which has a high potential wind power, is considered. In modern power systems, the extra short-term forecasting is also needed; therefore, in this study, wind speed forecasting in one hour and 10-minute intervals are employed. Input data are selected based on Grey Correlation Analysis (GCA), and according to the GCA result, in addition to wind speed data at various heights, environmental data such as humidity and temperature are also considered. To evaluate the efficiency of the proposed method, the simulation results are compared with other structures of ANNs and benchmarking machine learning methods such as Support Vector Machine (SVM) and Autoregressive Moving Average (ARMA) methods in different scenarios. In this comparison, the impact of DL has been thoroughly investigated. In this paper, the comparison of the performance of deep and shallow network structures has been studied.

Time series air quality forecasting with R Language and R Studio

The purpose of this study is to demonstrate how to make air quality forecasting to predict the Nitrogen Dioxide quality index in the future. In this paper, we demonstrate exploratory data analysis and compare the performance of the Autoregressive Integrated Moving Average and Exponential Smoothing Model. We used R Language and R Studio to integrate all the datasets, exploratory data analysis, data preparation, performing Autoregressive Integrated Moving Average and Exponential Smoothing methods, model evaluation, and visualization. This study used data from the automatic remote air quality-monitoring station located in an urban area in Madrid, Spain. The dataset in the period from 1 January 2001 to 31 December 2017. The dataset recorded six pollutants such as Nitrogen Dioxide, Particulate Matter 10 micrometres, Sulphur Dioxide, Carbon Monoxide, Ozone and Particulate Matter 2.5 micrometres. In this study, we focus only on Nitrogen Dioxide pollutants. From our model, we saw that exponential smoothing has better accuracy compared to the Autoregressive Integrated Moving Average. We also exposed that Nitrogen Dioxide pollutant shows unhealthy for sensitive group’s level in November to March and has the lowest level in June and July.

Peramalan harga beras IR64 kualitas III menggunakan metode Multi Layer Perceptron, Holt-Winters dan Auto Regressive Integrated Moving Average

This paper aims to present the analysis of price movements of IR64 quality III at the Cipinang Rice Main Market (PIBC) and the accuracy comparison of forecasting using Multi Layer Perceptron (MLP), Holt-Winters, and Auto Reggressive Integrated Moving Average (ARIMA) method. The data are daily price from 1 January 2016 to 31 May 2018 sourced from PT. Food Station. The analysis shows that the price of IR64 quality III rice tends to rise towards the end of 2016 and 2017. This is related to the decrease in the level of rice supply by January each year which encourages PT Food Station to conduct market operations to control the price of rice in the market. The results of accuracy comparison show that the MLP produces a value of Root Mean Square Error (RMSE) of 5,67, Holt-Winters exponential smoothing with trend and additive seasonal component produces a value RMSE of 70.71 and ARIMA method with parameters (1,1,2) resulted in RMSE values ​​of 58.71. The RMSE values ​​of the MLP method have smaller values ​​than the Holt Winter and ARIMA methods which indicate that the MLP method is more accurate

Fractal behavior of BDS-2 satellite clock offsets and its application to real-time clock offsets prediction

The satellite clocks of the BeiDou Navigation Satellite System (BDS) have characteristic differences compared to those of the global positioning system and Galileo. Therefore, the satellite clock offsets prediction method of BDS is different from those of these two systems. The basis for establishing a more appropriate prediction model is to clarify the statistical characteristics of the BDS satellite clock offsets, which can be reflected by fitting residuals of precise clock errors. Fractal behavior is generally not considered in existing studies. In this study, the rescaled range analysis method is improved by using the moving-average method in order to verify the fractal behavior of the BDS clock offsets. The computation results of the Hurst exponent show that the BDS clock offsets are fractal series with long-term memory, and the memory spans are obtained by V-statistic calculation. The quadratic polynomial fitting residuals of BDS clock offsets are fitted by using the periodic model and fractal interpolation model, where the latter approach has a higher accuracy. In the predictive modeling process, the quadratic polynomial forecasting model is improved by using the memory span, so that the forecasting model not only inherits the overall clock offsets trend but also considers the local memory trend. The fractal interpolation prediction model of the clock offsets is established by using the extension method of the affine iteration function system. The experimental results show that the average prediction accuracy of the fractal model in 3, 6, 12, and 24 h can reach 1.4890, 2.0222, 3.1609, and 4.9278 ns, which is 57.74%, 50.20%, 52.66%, and 49.42% higher than the products from the China iGMAS ultra-rapid prediction, respectively.

A New Ionospheric Model for Single Frequency GNSS User Applications Using Klobuchar Model Driven by Auto Regressive Moving Average (SAKARMA) Method Over Indian Region

The single-frequency users of Global Navigation Satellite System (GNSS) require an effective mathematical model that mitigate the dominant errors due to ionospheric delays. Klobuchar model approximately reduces ionospheric effect up to 50% through its coefficients in the navigation message, which is not sufficient for the GNSS single frequency users at critical applications. Hence, a new model, for Single Frequency GNSS User Applications using Klobuchar model driven by Auto Regressive Moving Average Method (SAKARMA) is proposed to forecast and enhance the precision of ionospheric delay estimations for GNSS users. The hourly VTEC maps are obtained from the Assimilated Indian Regional Vertical Total Electron Content (AIRAVAT) by the process of data assimilation using the Kalman filter exclusively for the Indian region (longitude: 65°E to 100°E; latitude: 5°N to 40°N) using 26 GPS TEC stations over Indian region. The accuracy of the SAKARMA model is investigated using the AIRAVAT maps for various Indian geographic regions during both geomagnetic quiet and disturbed conditions of September month in 2016 year. Furthermore, in order to test SAKARMA model, a dual-frequency Navigation with Indian Constellation (NavIC) receiver located at the KL Education Foundation, Guntur, India (geographic: 16.37°N, 80.37°E; geomagnetic: 7.44°N, 153.75°E) is used to collect the observations during 2 - 12 September 2017. Furthermore, SAKARMA model is also validated with Klobuchar model, Klobuchar-style coefficients provided by the Center for Orbit Determination in Europe (CODE) (CODKlob) Model, BeiDou System (BDS2) Model and NeQuick 2 Model over a low latitude NavIC station in forecasting the ionospheric delays. The experimental results of SAKARMA for NavIC have revealed that the MAPE for proposed SAKARMA model is 9-17% (accuracy: 83-91%), while 34-53% (accuracy: 47-66%) for the Klobuchar model. Thus, the results illustrate that the proposed SAKARMA model is capable of predicting the ionospheric delays for single frequency GNSS/NavIC users.

Forecasting and asymmetric volatility modeling of sustainability indexes in India

Sustainability is the new approach of corporations of the world over which is catching a lot of attention due to its divergence from the short-term approach to the long-term horizon. Sustainability indexes, that represent a set of companies for being socially responsible in terms of its corporate approach, need to be assessed in terms of forecasting the return as well as volatility of these returns. Autoregressive nature of three sustainability indexes, viz, Greenex, Carbonex and ESG index has been captured using autoregressive integrated moving averages method. The residuals of the model are subjected to generalized autoregressive conditional heteroscedasticity modeling to address volatility clustering. ARIMA results of three indices specify AR (1) for forecasting Carbonex is AR (1), MA (3) for forecasting ESG and AR (3) MA (3) for forecasting Greenex. Variances are changing as well as are a function of its past behavior, as shown by GARCH (1,1) process in the case of Carbonex and Greenex. Whereas in the case of ESG GARCH (1,1) does not explain such variance in residuals which could possibly be due to the presence of other exogenous factors in the time series. These results find place in the area of asset pricing and risk management of sustainability indexes in India. The research is based on the works of Joshi, Pandey, and Ross (2017), and it contributes to findings of Makridakis, Wheelwright, and Hyndman (1998).

Peramalan Rate of Return Saham Menggunakan Metode Brown’s Weighted Exponential Moving Average dengan Optimasi Levenberg-Marquardt

Stocks can be defined as a sign of the participation of unilateral capital in a company or a limited liability company in the form of proof of a company's assets and it is formed as a valueble proof letters as a statement of participating in capital. Return stockis one of the factors that motivates investors to invest or interact and also a reward for the courage of investors to take risks or investments. PT Bank Rakyat Indonesia is one of the largest banks in Indonesia that experiences an increase in stock prices every year. The Brown's Weighted Exponential Moving Average (BWEMA) method which is a combination of the Weighted Exponential Moving Average (WEMA) and Brown's Double Exponential Smoothing (BDES) methods will be used in this study. The data used is the daily data of closing price of the stock (closing price) of PT. Bank Rakyat Indonesia on February 6, 2018 until February 6, 2019. MSE and MAPE BWEMA values ​​were obtained at 6124.222 and 1.831685%, while MSE and MAPE WEMA values ​​were 7559.211 and 1.998439% respectively. The results obtained show that the BWEMA method has smaller MSE and MAPE values. This shows that the BWEMA method is better than the WEMA method in terms of forecasting. As a results the BWEMA method is continued to calculate the forecasts rate of return for the next 7 days. The average value returns obtained is 0.111497% stock which means during the period of the investment, investors get a profit of 0.111497%% per day of the total funds invested in the shares of PT Bank Rakyat Indonesia (Persero) Tbk.

Data Analysis Time Series For Forecasting The Greenhouse Effect

<span lang="EN-US">The greenhouse effect is a term used to describe the earth having a greenhouse effect where the sun's heat is trapped by the earth's atmosphere. This study aims to model the greenhouse effect and then predict the greenhouse effect in the coming period using the Autoregressive Integrated Moving Average (ARIMA) method. In this case, time series analysis and reference data for 31 months are used, from the period January 2017 - July 2019, the results of the ARIMA model that are suitable for forecasting the greenhouse effect are ARIMA (4.2.0) with Mean Square Error (MSE) of 161885</span>

Forecasting GDP Growth Rates of Bangladesh: An Empirical Study

The Gross Domestic Product (GDP) is the market value of all goods and services produced within the boundary of a nation in a year. This paper aims to apply time series tools and forecast GDP growth in the Bangladesh economy. Forecasting of time series is an important topic in macroeconomics. We collected the data from World Development Indicators (WDI) and it has been collected over a period of 37 years by WDI, World Bank. Augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) tests were applied to investigate the stationary character of the data. Stata and R statistical software was used to build a class of Autoregressive Integrated Moving Average (ARIMA) and exponential smoothing methods to model the GDP growth. We applied several ARIMA (P, I, Q) models and employed the ARIMA (1,1,1) model as best for forecasting. This ARIMA (1,1,1) model was chosen based on the minimum values of the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Also, we applied the Exponential Smoothing to forecast the GDP growth rate. In addition, among the Exponential Smoothing models, the triple exponential model better analyzed the data based on lowest Sum of Square Error (SSE) and Root Mean Square Error (RMSE). Using these models, the values of future GDP growth rates are forecasted. Statistical results show that Bangladesh’s GDP growth rate is an increasing trend that will continue rising in the future. This finding will help policymakers and academicians to formulate economic and business strategies more precisely . Keywords: Stationary time series, ARIMA, Time Series Forecasting, Exponential Smoothing, GDP growth rate, GDP growth in Bangladesh DOI : 10.7176/JESD/10-23-02 Publication date: December 31 st 2019