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

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.

Download Full-text

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

PERFORMA Media Ilmiah Teknik Industri ◽

10.20961/performa.14.2.10986 ◽

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

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

Download Full-text

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

JIEMS (Journal of Industrial Engineering and Management Systems) ◽

10.30813/jiems.v14i1.2418 ◽

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

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.

Download Full-text

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

International Journal of Applied Logistics ◽

10.4018/ijal.2014040106 ◽

2014 ◽

Vol 5 (2) ◽

pp. 74-86 ◽

Cited By ~ 4

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.

Download Full-text

Design Features of Forecasting Systems Involving Management Judgments

Journal of Marketing Research ◽

10.1177/002224378502200401 ◽

1985 ◽

Vol 22 (4) ◽

pp. 353-364 ◽

Cited By ~ 7

Author(s):

Mark M. Moriarty

Keyword(s):

Time Series ◽

Pattern Recognition ◽

Control System ◽

System Design ◽

Forecasting Model ◽

Design Features ◽

Forecasting Methods ◽

Forecasting Method ◽

System Use ◽

Forecasting System

A set of forecasting system design features is proposed to detect, measure, and reduce bias in forecasts and to determine the need for retention of forecasting methods used by an organization. The system is flexible enough to be incorporated into a variety of forecasting environments as part of a marketing control system. Use is made of several statistics that measure forms of forecasting bias including lack of time-series pattern recognition and over-optimism (over-pessimism). The system also uses a criterion for retention of a forecasting method that is based on composite forecasting model procedures. An empirical application illustrates the advantages of the proposed design features in correcting management judgment forecast methods which are prone to bias and in assessing the need for retention of forecasting methods.

Download Full-text

A Neutrosophic Forecasting Model for Time Series Based on First-Order State and Information Entropy of High-Order Fluctuation

Entropy ◽

10.3390/e21050455 ◽

2019 ◽

Vol 21 (5) ◽

pp. 455 ◽

Cited By ~ 5

Author(s):

Hongjun Guan ◽

Zongli Dai ◽

Shuang Guan ◽

Aiwu Zhao

Keyword(s):

Time Series ◽

Information Entropy ◽

Stock Exchange ◽

Time Series Forecasting ◽

High Order ◽

Stock Index ◽

Neutrosophic Set ◽

Forecasting Model ◽

Forecasting Models ◽

Proposed Model

In time series forecasting, information presentation directly affects prediction efficiency. Most existing time series forecasting models follow logical rules according to the relationships between neighboring states, without considering the inconsistency of fluctuations for a related period. In this paper, we propose a new perspective to study the problem of prediction, in which inconsistency is quantified and regarded as a key characteristic of prediction rules. First, a time series is converted to a fluctuation time series by comparing each of the current data with corresponding previous data. Then, the upward trend of each of fluctuation data is mapped to the truth-membership of a neutrosophic set, while a falsity-membership is used for the downward trend. Information entropy of high-order fluctuation time series is introduced to describe the inconsistency of historical fluctuations and is mapped to the indeterminacy-membership of the neutrosophic set. Finally, an existing similarity measurement method for the neutrosophic set is introduced to find similar states during the forecasting stage. Then, a weighted arithmetic averaging (WAA) aggregation operator is introduced to obtain the forecasting result according to the corresponding similarity. Compared to existing forecasting models, the neutrosophic forecasting model based on information entropy (NFM-IE) can represent both fluctuation trend and fluctuation consistency information. In order to test its performance, we used the proposed model to forecast some realistic time series, such as the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), the Shanghai Stock Exchange Composite Index (SHSECI), and the Hang Seng Index (HSI). The experimental results show that the proposed model can stably predict for different datasets. Simultaneously, comparing the prediction error to other approaches proves that the model has outstanding prediction accuracy and universality.

Download Full-text

Load forecasting of refrigerated display cabinet based on CEEMD–IPSO–LSTM combined model

Open Physics ◽

10.1515/phys-2021-0043 ◽

2021 ◽

Vol 19 (1) ◽

pp. 360-374

Author(s):

Yuan Pei ◽

Lei Zhenglin ◽

Zeng Qinghui ◽

Wu Yixiao ◽

Lu Yanli ◽

...

Keyword(s):

Time Series ◽

Deep Learning ◽

Time Series Data ◽

Load Forecasting ◽

Series Data ◽

Forecasting Model ◽

Combined Model ◽

Forecasting Accuracy ◽

Forecasting Method ◽

Consumption Reduction

Abstract The load of the showcase is a nonlinear and unstable time series data, and the traditional forecasting method is not applicable. Deep learning algorithms are introduced to predict the load of the showcase. Based on the CEEMD–IPSO–LSTM combination algorithm, this paper builds a refrigerated display cabinet load forecasting model. Compared with the forecast results of other models, it finally proves that the CEEMD–IPSO–LSTM model has the highest load forecasting accuracy, and the model’s determination coefficient is 0.9105, which is obviously excellent. Compared with other models, the model constructed in this paper can predict the load of showcases, which can provide a reference for energy saving and consumption reduction of display cabinet.

Download Full-text