scholarly journals Demand Forecasting of Toyota Avanza Cars in Indonesia: Grey Systems Approach

2021 ◽  
Vol 1 (1) ◽  
pp. 38-47
Author(s):  
Fitrah Amalia Arsy
Keyword(s):  
Systems Approach ◽  
Demand Forecasting ◽  
Grey Model ◽  
Percentage Error ◽  
Developing Strategy ◽  
Forecasting Model ◽  
Exponential Regression ◽  
Grey Forecasting ◽  
Car Sales ◽  
The Mean

Toyota Avanza car is a popular four-wheeler among Indonesia middle-class customers. The current study aims to forecast the demand for Toyota Avanza cars in Indonesia in the next six years using the grey forecasting model EGM (1,1, α, θ). The comparative analysis of the results obtained from the grey model with those of Linear Regression, Exponential Regression, and Exponential Triple Smoothing techniques revealed the superiority of the grey model as it produced most accurate forecasts. The accuracy was measured through the Mean Absolute Percentage Error. The results revealed, the car sales are likely to decline in the future. Although forecasts are never completely accurate, forecasting can provide a reference for developing strategy to meet future demand. The results are important for Toyota Avanza car manufacturers in Indonesia.  

Grey Forecasting of the Exports of Indonesian Palm Oil to India

2021 ◽  
Vol 1 (2) ◽  
pp. 60-68
Author(s):  
Ferta Monamaulisa Septyari
Keyword(s):  
Palm Oil ◽  
Secondary Data ◽  
Percentage Error ◽  
Forecasting Model ◽  
Error Measurement ◽  
Policy Makers ◽  
Exponential Regression ◽  
Grey Forecasting ◽  
Oil Exports ◽  
Increasing Trend

Palm oil is one of the leading export commodities of Indonesia. Knowing demand in advance can help policy-makers better prepare for the situation. India is one of the major importers of Indonesian palm oil. The study forecasted the Indonesian palm oil's exports to India from till 2025 using the grey forecasting model EGM (1,1, α, θ). The comparative analyses with Linear regression and exponential regression showed that the grey forecasting technique is relatively more accurate to forecast palm oil exports despite huge uncertainty in the data trend. The secondary data on Indonesian palm oil exports to India from 2011-2018 was obtained from the Indonesian Central Statistics Agency (BPS). Mean absolute percentage error was used for error measurement. Despite uncertainty in data, the results show an increasing trend in palm oil exports.  

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.

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 Grey Forecasting of Inbound Tourism to Bali and Financial Loses from the COVID-19

2021 ◽  
Vol 1 (1) ◽  
pp. 48-57
Author(s):  
Irsyad Yoga ◽  
I Gede Agus Yudiarta
Keyword(s):  
Service Providers ◽  
Tourism Industry ◽  
Grey Model ◽  
Forecasting Model ◽  
Financial Loss ◽  
Policy Makers ◽  
Industry Sector ◽  
Grey Forecasting ◽  
Inbound Tourism ◽  
Statistical Agency

Management and planning in the Indonesian tourism industry is an important matter. It involves responding to changes and uncertain conditions, especially in the tourism industry sector in Bali, Indonesia. Bali is a tourist spot that relies on foreign tourists. When a situation is not conducive, such as the COVID-19 outbreak that befell unexpectedly, proper management and planning are challenging without accurate forecasts. The current study used the Even Grey Forecasting model EGM (1,1,α,θ) to forecast the number of tourists to Bali, a famous tourist spot in Indonesia, and the approximate financial loss incurred from the pandemic in 2020 is quantified. These objectives are achieved through the data collected from the Bali statistical agency and analyzed through the grey model and some mathematical computations. The results indicated that the pandemic's impact on inbound tourism was severe, and the economy needs some time to recover. The study reported a loss of more than $7.3 billion to Bali due to the COVID-19 outbreak. It is possibly the first study of its kind, and its findings are important for the policy-makers, Tour & Travel service providers, and tourism-related businesses.  

The Analysis and Forecast about the Supply and Demand of China's Rubber Raw Materials Based on Grey Model

2013 ◽  
Vol 404 ◽  
pp. 796-801
Author(s):  
Zhao Jun Wang ◽  
Zhou Lin ◽  
Shuai Liu
Keyword(s):  
Natural Rubber ◽  
National Economy ◽  
Raw Materials ◽  
Supply And Demand ◽  
Grey Model ◽  
Forecasting Model ◽  
Rubber Industry ◽  
Grey Forecasting ◽  
Synthetic Rubber ◽  
The Status

The rubber industry is an important sector in the national economy. The article took the natural rubber and synthetic rubber as the main studying objects to analyze and forecast the amount of supply and demand of Chinas rubber raw materials. Analyzed the status of supply and demand of Chinas rubber raw materials from 2006 to 2011, and established the Grey Forecasting Model to forecast the supply and demand from 2012 to 2017 in China, and concluded that the prosperous supply and demand of rubber raw materials would be continued in the future.

scholarly journals Using a Novel Grey System Model to Forecast Natural Gas Consumption in China

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Lifeng Wu ◽  
Sifeng Liu ◽  
Haijun Chen ◽  
Na Zhang
Keyword(s):  
Grey Model ◽  
Forecasting Model ◽  
Limited Data ◽  
Grey System ◽  
Data Set ◽  
Grey Forecasting ◽  
Grey Forecasting Model ◽  
Energy Needs ◽  
New Information ◽  
Gas Consumption

Accurate prediction of the future energy needs is crucial for energy management. This work presents a novel grey forecasting model that integrates the principle of new information priority into accumulated generation. This grey model can better reflect the priority of the new information theoretically. The results of two practical examples demonstrate that this grey model provides very remarkable short-term predication performance compared with traditional grey forecasting model for limited data set forecasting. It is applied to Chinese gas consumption forecasting to show its superiority and applicability.

scholarly journals Forecasting Carbon Dioxide Emissions for Singapore using Grey Model with Cramer’s Rule

2021 ◽  
Vol 17 (4) ◽  
pp. 437-445
Author(s):  
Assif Shamim Mustaffa Sulaiman ◽  
Ani Shabri
Keyword(s):  
Carbon Dioxide ◽  
Global Warming ◽  
Carbon Dioxide Emissions ◽  
Grey Model ◽  
Forecasting Model ◽  
Fossil Carbon ◽  
Forecasting Models ◽  
Grey Forecasting ◽  
Grey Forecasting Model ◽  
Cramer’S Rule

This article analyses and forecasts carbon dioxide () emissions in Singapore for the 2012 to 2016 period. The study analysed the data using grey forecasting model with Cramer’s rule to calculate the best SOGM(2,1) model with the highest accuracy of precision compared to conventional grey forecasting model. According to the forecasted result, the fitted values using SOGM(2,1) model has a higher accuracy precision with better capability in handling information to fit larger scale of uncertain feature compared to other conventional grey forecasting models. This article offers insightful information to policymakers in Singapore to develop better renewable energy instruments to combat the greater issues of global warming and reducing the fossil carbon dioxide emissions into the environment.

Forecasting of Production and Consumption of Ethanol in Thailand Using Time Series Analysis

2015 ◽  
Vol 781 ◽  
pp. 651-654
Author(s):  
Sasiwimon Sriyotha ◽  
Rojanee Homchalee ◽  
Weerapat Sessomboon
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Ethanol Production ◽  
Agricultural Products ◽  
Percentage Error ◽  
Forecasting Model ◽  
Forecasting Models ◽  
Series Analysis ◽  
Production And Consumption ◽  
The Mean

Ethanol is the important renewable energy in Thailand. It is alcohol produced from sugarcane and tapioca that are agricultural products available in Thailand. Ethanol is used to blend with gasoline for use as gasohol. Ethanol production and consumption in Thailand are fluctuating. Consequently, planning of ethanol production and consumption is irrelevant. In order to solve this problem, this study aims to find forecasting models using time series analysis including exponential smoothing and the Box-Jenkins methods. The most appropriate forecasting model was selected from the two methods by considering the minimum of the mean absolute percentage error: MAPE. It was found that the Box-Jenkins is the most appropriate method to forecast both ethanol production and consumption. The forecasting results were then used to determine appropriate quantity and proportion of molasses and tapioca needed for ethanol production in the future.

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