scholarly journals Aplikasi Metode Grey Forecasting Pada Peramalan Kebutuhan Bahan Bakar Alternatif Ramah Lingkungan di PT. Indocement Tunggal Prakarsa Tbk

2015 ◽  
Vol 14 (2) ◽  
Author(s):  
Nanda Lokita Nariswari ◽  
Cucuk Nur Rosyidi
Keyword(s):  
Time Series ◽  
Alternative Energy ◽  
Historical Data ◽  
Raw Materials ◽  
Time Series Forecasting ◽  
Short Term ◽  
Grey Forecasting ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
A Company

<span><em>Forecasting is one of the methods required by a company to plan the demand of raw materials in the </em><span><em>future, in order to avoid the emergence of various problems such as stock out. However, not all </em><span><em>forecasting methods can be used to forecast demand in the short term a specially a condition where the </em><span><em>company only has a few historical data. Grey method is a forecasting method which can be used to </em><span><em>predict the short-term demand. The purpose of this study is to determine how well the Grey method used </em><span><em>to predict the demand of alternative energy and compared with other forecasting methods. Mean Squared </em><span><em>Error (MSE) is used as a measure of the goodness of the method. The result of the study indicates that the </em><span><em>Grey Forecasting Methods MSE value that is smaller than other time series forecasting methods.</em></span></span></span></span></span></span></span><br /></span>

scholarly journals Proposed Improvement of Forecasting Using Time Series Forecasting of Fast Moving Consumer Goods

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Tasya Regina ◽  
Panca Jodiawan
Keyword(s):  
Time Series ◽  
Historical Data ◽  
Moving Average ◽  
Time Series Forecasting ◽  
Class A ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
Double Exponential ◽  
Data Points ◽  
Double Exponential Smoothing

<p>The company discussed in this paper is a national distributor firm that distributes FMCG products. The PPIC division in the company is responsible for forecasting the demand using the combination of the moving average method and intuition according to the interest of the company. However, the PPIC staff never measures the accuracy of their forecasting method. This research paper aims to evaluate the forecasting methods used to predict the demands of 12 classes of A SKU. Four-time series forecasting methods are particularly implemented, i.e., ARIMA, moving average (MA), double exponential smoothing (DES), and linear regression (RL). Forecasting using the ARIMA method is carried out by considering the stationarity of the average and variance of the historical data points. Forecasting using DES is carried out by using the optimal alpha and gamma values of the ARIMA method. The results show that the performance of each forecasting method varies, depending on which demands of class A SKU are predicted. Based on these results, the current forecasting method utilized by the company should be improved using the time series forecasting methods leading to the smallest error values for each class of A SKU.</p>

scholarly journals APLIKASI METODE ARIMA BOX-JENKINS UNTUK MERAMALKAN KASUS DBD DI PROVINSI JAWA TIMUR

2019 ◽  
Vol 13 (2) ◽  
pp. 183
Author(s):  
Muhammad Bintang Pamungkas
Keyword(s):  
Time Series ◽  
Significance Test ◽  
Time Series Forecasting ◽  
Forecasting Model ◽  
Upward Trend ◽  
Independent Variables ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
Dependent Variables ◽  
Seasonal Data

The Box-Jenkins forecasting method is one of the time series forecasting methods. This method uses past values as dependent variables and independent variables are ignored. Box-Jenkins (ARIMA) method has advantages that can be used on non-stationary data, can be used on all data patterns including seasonal data patterns so this method can be used to predict cases of DHF in East Java Province. This research was conducted to determine the best model with seasonal ARIMA forecasting model and also to analyze the result of DHF case forecasting in East Java Province. The analysis result shows that the best model for DHF case in East Java Province is ARIMA (1,1,2)(2,1,1)12. The best model has fulfilled the test requirement that is parameter significance test and diagnostics check. Forecasting results show the number of DHF cases in 2017-2018 will experience an upward trend. The total number of DHF cases in 2017 was 14,277 cases and increased to 22,284.54 DHF cases in 2018. The forecasting results showed that the highest peak of DHF cases occurred in January 2017 with 1,914.22 cases and then decrease in the next month until the lowest case occurred in October with 768.46. The forecast for 2018 also shows that the highest DHF cases occurred in January with 3455.55 and declined to the lowest in October with 1126.49 cases. MAPE value in the forecast is 43.51%. The MAPE value indicates that the forecasting is good enough, adequate and feasible to use.

scholarly journals Analogous Forecasting of Products with a Short Life Cycle

2010 ◽  
Vol 4 (2) ◽  
pp. 71-85 ◽  
Author(s):  
Natalia Szozda
Keyword(s):  
Time Series ◽  
Supply Chain ◽  
Life Cycle ◽  
Historical Data ◽  
Life Cycles ◽  
Time Series Forecasting ◽  
High Tech ◽  
Short Life ◽  
Forecasting Methods ◽  
Fashion Apparel

Managing a supply chain for products with a short life cycle, like fashion apparel, high-tech, personal computers, toys, CD’s etc., is challenging for many companies (Fisher and Raman, 1999). Because the life cycles of these products are too short for standard time- series forecasting methods (not longer than one – two years), an important way of overcoming the challenges of managing supply chains for such products is to find appropriate forecasting methodologies. The standard forecasting methods require some historical data, which are often unavailable at the time when the forecasts are being performed for products with a short life cycle (Lin, 2005). The method described in this article allows forecasters to use life cycles of similar, analogous products to arrive at the initial forecasts for the product(s) at hand.

Forecasting Demand with Support Vector Regression Technique Combined with X13-ARIMA-SEATS Method in the Presence of Calendar Effect

2014 ◽  
Vol 5 (2) ◽  
pp. 74-86 ◽  
Author(s):  
Malek Sarhani ◽  
Abdellatif El Afia
Keyword(s):  
Time Series ◽  
Supply Chain ◽  
Support Vector Regression ◽  
Case Studies ◽  
Time Series Forecasting ◽  
Support Vector ◽  
Reliable Prediction ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
Two Stages

In order to better manage and optimize supply chain, a reliable prediction of future demand is needed. The difficulty of forecasting demand is due mainly to the fact that heterogeneous factors may affect it. Analyzing such kind of data by using classical time series forecasting methods, will fail to capture such dependency of factors. This paper is released to present a forecasting approach of two stages which combines the recent methods X13-ARIMA-SEATS and Support Vector Regression (SVR). The aim of the first one is to remove the calendar effect, while the purpose of the second one is to forecast the demand after the removal of this effect. This approach is applied to three different case studies and compared to the forecasting method based on SVR alone.

APPLING GREY FORECASTING METHOD TO FORECAST THE PORTFOLIO’S RATE OF RETURN IN STOCK MARKET OF IRAN

2012 ◽  
Vol 01 (07) ◽  
pp. 01-16
Author(s):  
Ali Mohammadi ◽  
Sara Zeinodin Zade
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Predictive Ability ◽  
Grey Model ◽  
Rate Of Return ◽  
Forecasting Accuracy ◽  
Grey Forecasting ◽  
Best Value ◽  
Forecasting Method ◽  
Time Series Method

Stock market is one of the most important investment market, which influenced by many factors, therefore it needs a robust and accurate forecasting. In this study ,grey model used as a forecasting method and examined if it is the most reliable forecasting method in comparison of time series method. The information of portfolio’s rate of return is gathered from 50 accepted companies in Tehran stock market, which were announced as the best companies last year. Mean Square of the errors (MSE) is computed by different value of α in grey model which could be varied between .1 to .9 ,to examined if α=.5 is the best value that our model could take .Then the predictive ability of the model is compared with different type of time series based forecasting methods Experimental results confirm forecasting accuracy of grey model. Tracking signal is computed for grey model to see whether grey model forecasting is in control or not. At the last portfolio’s rate of return is forecasted for next periods.

Sign in / Sign up

Export Citation Format

Share Document