scholarly journals GOLD PRICESFORECASTING USING TRIPLE EXPONENTIAL METHOD

2021 ◽  
Vol 1 (2) ◽  
pp. 269-276
Author(s):  
Khairawati Khairawati ◽  
Wahyu Fuadi ◽  
Rizki Ramadhansyah ◽  
Dedi Fariadi
Keyword(s):  
Exponential Smoothing ◽  
Gold Price ◽  
Percentage Error ◽  
Price Forecasting ◽  
A Value ◽  
Gold Market ◽  
Exponential Method ◽  
The Mean ◽  
Parameter Values ◽  
Nonlinear Nature

Governments, organizations, and citizens have taken an interest in gold price fluctuations. Gold price forecasting that is accurate may effectively capture price shift tendencies and reduce the effects of gold market volatility. However, due to the multi-factor and nonlinear nature of the gold market. The triple exponential smoothing strategy is used in this study to predict the rise in a value over time since it can replicate trends and seasonal patterns. according to the gold price swings pattern and seasonal components at the same time To calculate system accuracy, the Mean Absolute Percentage Error is employed (MAPE). With alpha 0.15 and beta 0.85 as parameter values, the triple exponential smoothing (TES) approach achieves an accuracy rate of 86.93 percent and a MAPE of 12.49 percent in this study.

scholarly journals Penerapan Ukuran Ketepatan Nilai Ramalan Data Deret Waktu dalam Seleksi Model Peramalan Volume Penjualan PT Satriamandiri Citramulia

2011 ◽  
Vol 2 (2) ◽  
pp. 636 ◽  
Author(s):  
Iwa Sungkawa ◽  
Ries Tri Megasari
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Forecast Accuracy ◽  
Exponential Smoothing ◽  
Series Data ◽  
Percentage Error ◽  
Forecasting Model ◽  
Data Series ◽  
Past Data ◽  
The Mean

Forecasting is performed due to the complexity and uncertainty faced by a decision maker. This article discusses the selection of an appropriate forecasting model with time series data available. An appropriate forecasting model is required to estimate systematically about what is most likely to occur in the future based on past data series, so that errors (the differences between what actually happens and the results of the estimation) can be minimized. A gauge is required to detect the required the value of forecast accuracy. In this paper ways of forecasting accuracy of detection are discussed using the mean square error (MSE) and the mean absolute percentage error (MAPE). The forecasting method uses Moving Average, Exponential Smoothing, and Winters method. With the three methods forecast value is determined and the smallest value of MSE and Mape is selected. The results of data analysis showed that the Exponential Smoothing is considered an appropriate method to forecast the sales volume of PT Satriamandiri Citramulia because it produces the smallest value of MSE and Mape. 

scholarly journals Forecasting the incidence of acute haemorrhagic conjunctivitis in Chongqing: a time series analysis

2020 ◽  
Vol 148 ◽  
Author(s):  
Hongfang Qiu ◽  
Dewei Zeng ◽  
Jing Yi ◽  
Hua Zhu ◽  
Ling Hu ◽  
...  
Keyword(s):  
Moving Average ◽  
Development Trend ◽  
Exponential Smoothing ◽  
Percentage Error ◽  
Peak Period ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Incidence Data ◽  
The Mean ◽  
Actual Incidence

Abstract Acute haemorrhagic conjunctivitis is a highly contagious eye disease, the prediction of acute haemorrhagic conjunctivitis is very important to prevent and grasp its development trend. We use the exponential smoothing model and the seasonal autoregressive integrated moving average (SARIMA) model to analyse and predict. The monthly incidence data from 2004 to 2017 were used to fit two models, the actual incidence of acute haemorrhagic conjunctivitis in 2018 was used to validate the model. Finally, the prediction effect of exponential smoothing is best, the mean square error and the mean absolute percentage error were 0.0152 and 0.1871, respectively. In addition, the incidence of acute haemorrhagic conjunctivitis in Chongqing had a seasonal trend characteristic, with the peak period from June to September each year.

Improved Grey Forecasting Model for Taiwans Green Accounting

2013 ◽  
Vol 404 ◽  
pp. 398-403 ◽  
Author(s):  
Ching I Lin ◽  
Shin Li Lu ◽  
Shih Hung Tai
Keyword(s):  
Prediction Accuracy ◽  
Economic Indicator ◽  
Percentage Error ◽  
Forecasting Model ◽  
Environmental Costs ◽  
Green Accounting ◽  
Grey Forecasting ◽  
A Value ◽  
Grey Forecasting Model ◽  
The Mean

This paper applies the grey forecasting model to forecast the green accounting of Taiwan from 2002 to 2010. Green accounting is an effective economic indicator of human environmental and natural resources protection. Generally, Green accounting is a type of accounting that attempts to factor environmental costs into the financial results of operations. This paper modifies the original GM(1,1) model to improve prediction accuracy in green accounting and also provide a value reference for government in drafting relevant economic and environmental policies. Empirical study shows that the mean absolute percentage error of RGM(1,1) model is 2.05% lower than GM(1,1) and AGM(1,1), respectively. Results are very encouraging as the RGM(1,1) forecasting model clearly enhances the prediction accuracy.

Forecasting Bitcoin using Double Exponential Smoothing Method Based on Mean Absolute Percentage Error

2020 ◽  
Vol 4 (2) ◽  
pp. 91
Author(s):  
Febri Liantoni ◽  
Arif Agusti
Keyword(s):  
Exponential Smoothing ◽  
Smoothing Method ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Business World ◽  
Absolute Percentage Error ◽  
A Value ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Exponential Smoothing Method

Abstract— After being introduced in 2008, the rise in the price of bitcoin and the popularity of other cryptocurrencies triggered a growing discussion about how much energy was consumed during the production of this currency. Making cryptocurrency the most expensive and most popular, both the business world and the research community have begun to study the devel-opment of bitcoin. In this study bitcoin price predictions are performed using the double exponential smoothing method based on the mean absolute percentage error (MAPE). The MAPE value is used to find the best alpha (α) parameter as the basis for bitcoin price forecasting. The dataset used is the price of bitcoin from 2017 to 2019. The dataset was obtained from www.cryptocompare.com. As for the value of the alpha parameter (α), using a value of 0.1 to 0.9. Based on the test results using the double exponential smoothing method obtained the smallest MAPE value of 2.89%, with the best alpha (α) at 0.9. The prediction is done to see the price of bitcoin on January 1, 2020. The error rate generated on the predicted price of bitcoin uses an amount of 0.0373%. This shows that the system built can be used as a support for decision making when trading bitcoin.

scholarly journals FIT OF STATISTICAL FORECASTING MODEL BERDASARKAN VARIABEL ANGKA KEMISKINAN DI PROVINSI KEPULAUAN BANGKA BELITUNG

2020 ◽  
Vol 9 (2) ◽  
pp. 117
Author(s):  
DESY YULIANA DALIMUNTHE
Keyword(s):  
Economic Development ◽  
Mean Squared Error ◽  
Exponential Smoothing ◽  
Poverty Rate ◽  
Percentage Error ◽  
Projection Model ◽  
A Value ◽  
Forecasting Method ◽  
Double Exponential ◽  
Double Exponential Smoothing

Poverty is one of the main problems in economic development and is considered to be a variable to measure the success of the economic development of a region. This study is limited to the analysis and determination of the best forecasting statistical model for the variable poverty rate in the Bangka Belitung Islands Province area based on R Square, Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) assessments. This study uses the Exponential Smoothing forecasting method which emphasizes the procedure of continuous improvement of the latest observation objects which hopefully can provide the appropriate results. In general, the double exponential smoothing model from Holt's is the best projection model compared to other exponential smoothing models for projecting poverty data in the Bangka Belitung Islands Province with historical data for 2002-2018 with an increase in projections in 2019 of 0.37 % with Upper Criteria Limit (UCL) of 1.07% and Lower Criteria Limit (LCL) of -0.33% with a value of R Square of 0.627 which means that the independent variable can explain the variance of the dependent variable of 62.7% of this model, and the value of RMSE is 0.328 and MAPE is 22.162. The results of this model when compared to other models have relatively larger R Squared values ??and smaller RMSE and MAPE values.

Application of Several Models for the Forecasting of the Container Throughput of the Abidjan Port in Ivory Coast

2017 ◽  
Vol 28 ◽  
pp. 157-168 ◽  
Author(s):  
Pascal Kany Prud’ome Gamassa ◽  
Yan Chen
Keyword(s):  
Ivory Coast ◽  
Research Work ◽  
Forecast Model ◽  
Exponential Smoothing ◽  
Percentage Error ◽  
Forecasting Model ◽  
Combination Forecast ◽  
Double Exponential ◽  
The Mean ◽  
Double Exponential Smoothing

The Abidjan Port in Ivory Coast has the second highest volume of Container Throughput in West Africa and aims to become a hub Port for the region and one of the most developed Port in Africa in the coming years. In this article, several Forecasting Models are applied to accurately forecast the Abidjan Port Container Throughput. These models are the Grey model, the Linear Regression model, the Double Exponential Smoothing model and a Combination Forecast model. After the application of each model, their results have been compared with the mean absolute percentage error. From their results, the Double Exponential Smoothing model has got the smallest error and been found to have the best data available on the research work, becoming at the same time, the best forecasting model for the Abidjan Port Container Throughput. A forecast of the Abidjan Port Container Throughput was finally made for the period covering the year 2015 up to the year 2020.

scholarly journals INFLUENCE OF TWO DIFFERENT ACCURACY IMPROVEMENTS TO NUMERICAL PRICE FORECASTING

2020 ◽  
Vol 7 (4) ◽  
pp. 253-260
Author(s):  
Marcela Lascsáková
Keyword(s):  
Differential Equation ◽  
Numerical Models ◽  
Mean Absolute Percentage Error ◽  
Initial Problem ◽  
Percentage Error ◽  
Price Forecasting ◽  
Initial Condition ◽  
Absolute Percentage Error ◽  
Initial Values ◽  
The Mean

The paper aims to compare two different strategies of accuracy improvement of studied prognostic numerical models. The price prognoses of aluminium on the London Metal Exchange were determined as the numerical solution of the Cauchy initial problem for the 1st order ordinary differential equation. To make the numerical model more accurate two ideas were realized, the modification of the initial condition value by the nearest stock exchange (initial condition drift) and different way of creation of the differential equation in solved Cauchy initial problem (using two known initial values). With regard to the accuracy of the determined numerical models, the model using two known initial values obtained slightly better forecasting results. The mean absolute percentage error of all observed forecasting terms was mostly less than 5%. This strategy was more successful in problematic price movements, especially at steep price increase and within significant changes in the price movements. Larger fluctuation of prognoses calculated by this model was disadvantageous in forecasting terms with a small error. Moderate increase of prognoses obtained by the model using initial condition drift better described price fluctuation. Both chosen strategies eliminated the forecasting terms with the mean absolute percentage error larger than 10%. Therefore, we recommend both strategies as acceptable way for commodity price forecasting.

scholarly journals Regresi Nonparametrik Kernel Gaussian pada Pemodelan Angka Kelahiran Kasar di Provinsi Nusa Tenggara Barat

2020 ◽  
Vol 3 (2) ◽  
pp. 100
Author(s):  
Deni Pratiwi ◽  
Lalu Abd Azis Mursy ◽  
Muhammad Rizaldi ◽  
Nurul Fitriyani
Keyword(s):  
Nonparametric Regression ◽  
Cross Validation ◽  
Simulated Annealing Algorithm ◽  
Gaussian Function ◽  
Distribution Patterns ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
A Value ◽  
The Mean ◽  
Annealing Algorithm

This study aims to model Crude Birth Rates (CBR) in West Nusa Tenggara Province. The nonparametric regression method was used in this research by considering data distribution patterns that do not show a linear relationship between variables. In this case, the kernel nonparametric regression using the Gaussian function and the Nadaraya-Watson estimator. The results showed optimal bandwidths of 0.55542837, 1.29042927, 0.94706041, and 0.92278896 with a value of minimum Generalized Cross-Validation (GCV) of 0.000000000432613511, which was minimized by the simulated annealing algorithm. The resulting model's accuracy can be seen from the coefficient of determination (R2) of 99.23% and the Mean Absolute Percentage Error (MAPE) of 0.007049%.

An overview of global gold market and gold price forecasting

2010 ◽  
Vol 35 (3) ◽  
pp. 178-189 ◽  
Author(s):  
Shahriar Shafiee ◽  
Erkan Topal
Keyword(s):  
Gold Price ◽  
Price Forecasting ◽  
Gold Market

scholarly journals SISTEM PREDIKSI PENGGUNAAN LISTRIK PELANGGAN DI PT.PLN (PERSERO) RAYON LAMONGAN AREA BOJONEGORO DENGAN METODE TRIPLE EXPONENTIAL SMOOTHING (BROWN)

2019 ◽  
Vol 1 (1) ◽  
pp. 36
Author(s):  
Maslucha Maslucha
Keyword(s):  
Human Life ◽  
Exponential Smoothing ◽  
Percentage Error ◽  
Prediction System ◽  
Test Analysis ◽  
Average Value ◽  
The Third ◽  
A Value ◽  
Data Prediction ◽  
Exponential Smoothing Method

Electricity is one of the means of fulfilling the needs of human life which is very important in this era. Excessive use of electricity will have an impact on the high use of electricity kWh. The process of recording kWh on the customer meter is carried out by officers from PLN who routinely visit the customer's homes once a month. The meter recording clerk cannot record when the customer's house cannot be reached resulting in empty customer kWh data. Prediction System Using Electricity Customers at PT. PLN Lamongan aims to determine the amount of electricity usage kWh of the customer for the next period. This research uses the Triple Exponential Smoothing method (Brown). The calculation is done on 10 different customers with 24 data, namely the use of electric kWh per period from January 2015 to December 2016 with 9 different alpha values, namely alpha 0.1 - 0.9 and uses a reference of 3 months, 6 months and 12 months before. Prediction results will be compared with the actual data of kWh to determine the failure value or error value in predictions using mean absolute deviaton (MAD) and mean absolute percentage error (MAPE). From the third average forecasting test analysis, it produces an average MAPE value of 3 months reference with an average value of 2.922%, 6 months reference with an average value of 3.092% and a 12-month reference with an average value of 4.175%. The smallest MAPE, which is a test using a 6-month reference, produces a value of 1.886% with alpha 0.1.

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