scholarly journals Analysis of Data Mining for Forecasting Total Goods Delivery with Moving Average Method

2017 ◽  
Vol 2 (1) ◽  
pp. 7 ◽  
Author(s):  
M. Azman Maricar ◽  
Putu Widiadnyana ◽  
I Wayan Arta Wijaya
Keyword(s):  
Data Mining ◽  
Average Method ◽  
Mean Absolute Error ◽  
Moving Average ◽  
Absolute Error ◽  
Test Method ◽  
Period Data ◽  
Strong Attachment ◽  
Forecasting Techniques

In the logistics and distribution of goods, the expedition service is necessary, because the expedition is an important part of a business that has a strong attachment to the distribution. The number of deliveries from an expedition per period is uncertain, sometimes the number increases or decreases. This may result in an imbalance between existing facilities and employees and the number of shipments from customers or company policies. To overcome this, required forecasting techniques that are able to predict total shipments, as well as predict which goods and products are the most widely sent. The moving average method using the last 5 period data is used as a way of forecasting. MAPE (Mean Absolute % Error) is used as a test method, and a result of 34 %, indicates that the method is feasible to use.

scholarly journals Detection of Tea Export Potentiality from Bangladesh in the World Market: An Autoregressive Integrated Moving Average (ARIMA) Approach

2020 ◽  
pp. 21-29
Author(s):  
A. U. Noman ◽  
S. Majumder ◽  
M. F. Imam ◽  
M. J. Hossain ◽  
F. Elahi ◽  
...  
Keyword(s):  
Mean Absolute Error ◽  
Moving Average ◽  
Arima Model ◽  
Absolute Error ◽  
World Market ◽  
Information Criteria ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Autoregressive Integrated Moving Average ◽  
The World

Export plays an important role in promoting economic growth and development. The study is conducted to make an efficient forecasting of tea export from Bangladesh for mitigating the risk of export in the world market. Forecasting has been done by fitting Box-Jenkins type autoregressive integrated moving average (ARIMA) model. The best ARIMA model is selected by comparing the criteria- coefficient of determination (R2), root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE) and Bayesian information criteria (BIC). Among the Box-Jenkins ARIMA type models for tea export the ARIMA (1,1,3) model is the most appropriate one for forecasting and the forecast values in thousand kilogram for the year 2017-18, 2018-19, 2019-20, 2020-21 and 2021-22, are 1096.48, 812.83, 1122.02, 776.25 and 794.33 with upper limit 1819.70, 1348.96, 1862.09, 1288.25, 1318.26 and lower limit 660.69, 489.78, 676.08, 467.74, 478.63, respectively. So, the result of this model may be helpful for the policymaker to make an export development plan for the country.

scholarly journals Time series prediction of under-five mortality rates for Nigeria: comparative analysis of artificial neural networks, Holt-Winters exponential smoothing and autoregressive integrated moving average models

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Daniel Adedayo Adeyinka ◽  
Nazeem Muhajarine
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Mean Absolute Error ◽  
Moving Average ◽  
Absolute Error ◽  
Mortality Rates ◽  
Childhood Mortality ◽  
Autoregressive Integrated Moving Average ◽  
Under Five ◽  
Deming Regression

Abstract Background Accurate forecasting model for under-five mortality rate (U5MR) is essential for policy actions and planning. While studies have used traditional time series modeling techniques (e.g., autoregressive integrated moving average (ARIMA) and Holt-Winters smoothing exponential methods), their appropriateness to predict noisy and non-linear data (such as childhood mortality) has been debated. The objective of this study was to model long-term U5MR with group method of data handling (GMDH)-type artificial neural network (ANN), and compare the forecasts with the commonly used conventional statistical methods—ARIMA regression and Holt-Winters exponential smoothing models. Methods The historical dataset of annual U5MR in Nigeria from 1964 to 2017 was obtained from the official website of World Bank. The optimal models for each forecasting methods were used for forecasting mortality rates to 2030 (ending of Sustainable Development Goal era). The predictive performances of the three methods were evaluated, based on root mean squared errors (RMSE), root mean absolute error (RMAE) and modified Nash-Sutcliffe efficiency (NSE) coefficient. Statistically significant differences in loss function between forecasts of GMDH-type ANN model compared to each of the ARIMA and Holt-Winters models were assessed with Diebold-Mariano (DM) test and Deming regression. Results The modified NSE coefficient was slightly lower for Holt-Winters methods (96.7%), compared to GMDH-type ANN (99.8%) and ARIMA (99.6%). The RMSE of GMDH-type ANN (0.09) was lower than ARIMA (0.23) and Holt-Winters (2.87). Similarly, RMAE was lowest for GMDH-type ANN (0.25), compared with ARIMA (0.41) and Holt-Winters (1.20). From the DM test, the mean absolute error (MAE) was significantly lower for GMDH-type ANN, compared with ARIMA (difference = 0.11, p-value = 0.0003), and Holt-Winters model (difference = 0.62, p-value< 0.001). Based on the intercepts from Deming regression, the predictions from GMDH-type ANN were more accurate (β0 = 0.004 ± standard error: 0.06; 95% confidence interval: − 0.113 to 0.122). Conclusions GMDH-type neural network performed better in predicting and forecasting of under-five mortality rates for Nigeria, compared to the ARIMA and Holt-Winters models. Therefore, GMDH-type ANN might be more suitable for data with non-linear or unknown distribution, such as childhood mortality. GMDH-type ANN increases forecasting accuracy of childhood mortalities in order to inform policy actions in Nigeria.

scholarly journals Predicting Daily Returns of Global Stocks Indices: Neural Networks vs Support Vector Machines

2019 ◽  
pp. 1-13 ◽  
Author(s):  
Jasleen Kaur ◽  
Khushdeep Dharni
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Support Vector Machines ◽  
Mean Square Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Support Vector ◽  
Mean Square ◽  
Data Mining Techniques ◽  
Vector Machines

Uniqueness in economies and stock markets has given rise to an interesting domain of exploring data mining techniques across global indices. Previously, very few studies have attempted to compare the performance of data mining techniques in diverse markets. The current study adds to the understanding regarding the variations in performance of data mining techniques across the global stock indices. We compared the performance of Neural Networks and Support Vector Machines using accuracy measures Mean Absolute Error (MAE) and R­­­­oot Mean Square Error (RMSE) across seven major stock markets. For prediction purpose, technical analysis has been employed on selected indicators based on daily values of indices spanning a period of 12 years. We created 196 data sets spanning different time periods for model building such as 1 year, 2 years, 3 years, 4 years, 6 years and 12 years for selected seven stock indices. Based on prediction models built using Neural Networks and Support Vector Machines, the findings of the study indicate there is a significant difference, both for MAE and RMSE, across the selected global indices. Also, Mean Absolute Error and Root Mean Square Error of models built using NN were greater than Mean Absolute Error and Root Mean Square Error of models built using SVM.

scholarly journals AZƏRBAYCANDA COVID-19 KORONAVİRUSUNA YOLUXMANIN PROQNOZLAŞDIRILMASI ÜÇÜN ARIMA MODELLƏRİNİN TƏTBİQİ

2021 ◽  
Vol 12 (1) ◽  
pp. 95-104
Author(s):  
Firəngiz Sadıyeva ◽  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Mean Absolute Error ◽  
Moving Average ◽  
Absolute Error ◽  
Mean Square ◽  
Autoregressive Integrated Moving Average

Məqalədə COVID-19 pandemiyasını proqnozlaşdırmaq üçün avtoreqressiv inteqrasiya edilmiş hərəkətli ortalama (ing. ARIMA. Autoregressive İntegrated Moving Average) modeli təklif edilmişdir. COVID-19 dünyada sürətlə yayılan və hazırda davam edən yeni növ pandemiyadır. Son dövrlərdə pandemiyaya yoluxanların sayı Azərbaycanda rekord həddə çatmışdır. Məhz bu səbəbdən COVID-19 pandemiyasının proqnozu məsələsinə baxılmışdır və bir neçə ayı əhatə edən real verilənlərlə eksperimentlərdə təklif edilmiş ARIMA modelinin COVID-19 zaman sıralarının proqnozlaşdırılması üçün müxtəlif parametrlərlə tətbiq edilmişdir. Verilənlər dedikdə, 22.01.2020 – 22.10.2020 tarixləri arasında Azərbaycan Respublikasının Səhiyyə Nazirliyi (www.sehiyye.gov.az) tərəfindən rəsmi olaraq qeydiyyata alınan gündəlik yoluxma hallarının sayı nəzərdə tutulur. Bu verilənlərdən istifadə etməklə, növbəti zaman aralığında ölkəmizdə baş verəcək yoluxma halları proqnoz edilmişdir. Bunun üçün ARIMA modelinə müxtəlif parametrlər verilmiş və uyğun olaraq hər bir modelin səhv dərəcəsi qiymətləndirilmişdir. Səhvin qiymətləndirilməsi üçün MAPE (Mean Absolute Persentace Error), MAE (Mean Absolute Error) və RMSE (Root Mean Square Error) funksiyaları istifadə edilib. Müqayisələr nəticəsində ən uyğun model seçilmişdir. Alınmış nəticələr ölkəmizdə pandemiya dövründə həm səhiyyə sistemi, həm də adi vətəndaşlar üçün vacib amildir. Əldə edilmiş nəticələr statistik metodların koronavirusa aid qeyri-stasionar zaman sıralarının proqnozlaşdırılmasının digər məsələlərə tətbiqində də məhsuldar ola biləcəyini təsdiqləyir.

scholarly journals Sistem Informasi Persediaan Barang Dengan Metode Weight Moving Average Berbasis Android Di Toko Awd Mranggen

2021 ◽  
Vol 14 (2) ◽  
pp. 208-215
Author(s):  
Zaenal Mustofa Zaenal ◽  
Muhammad Sholikhan ◽  
Bachtiar Aziz Mulki
Keyword(s):  
Average Method ◽  
Moving Average ◽  
Sales Data ◽  
The Future ◽  
Forecasting System ◽  
Period Data

The AWD Mranggen store is a store that is engaged in the sale of bags, belts, shoes with sales developments increasing from year to year, with fairly tight business competition, the AWD Mranggen store must be able to calculate the estimated number of items to be purchased based on previous sales data, the prediction is very influential on the decision to determine the number of items to be provided by the AWD Mranggen Store for the next sales period data. Inventory of goods that are not right cause some losses in terms of time and also costs, it is necessary to have a forecasting system. Forecasting is a technique to identify a model that can be used to predict conditions in the future. By using the weight moving average method, it can be seen that the error value is more than smaller than other methods and the estimated results can be more precise so that it can help owners make decisions in carrying out inventory.

scholarly journals PERAMALAN JUMLAH MAHASISWA MENGGUNAKAN MOVING AVERAGE

Telematika ◽  
2018 ◽  
Vol 15 (1) ◽  
pp. 67
Author(s):  
Hari Prapcoyo
Keyword(s):  
Mean Square Error ◽  
Mean Absolute Error ◽  
Average Length ◽  
Moving Average ◽  
Absolute Error ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Mean Square ◽  
Absolute Percentage Error ◽  
Weighted Moving Average

AbstractThe Process of using resources in higher education is influenced by the up and down of the number students. The purpose of this study is to predict the number of students who study in the department of informatics engineering UPN Veteran Yogyakarta for the next periods. This research, data is taken from forlap dikti for Informatics Engineering fom 2009 until 2016 at UPN Veteran Yogyakarta. The method that used to forecast the number of students is a Moving Average method consisting of: Single Moving Average (SMA), Weighted Moving Average (WMA) and Exponential Moving Average (EMA). This study will use the forecasting accuracy namely Mean Square Error (MSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) to select the best model to be used for forecasting. The best model that used for forecasting is Weighted Moving Average (WMA) with weighted 1/3 and average length (n) used for 2. The smallest value for MSE of 5807.96; the smallest MAE value of 55.89 and the smallest value for MAPE of 5.24%. Forecasting of the number of students for four semesters in the future after the even semester of 2016 are respectively: 902; 901,33; 901,56 and 901,48. Keywords : Forecasting, UPN Veteran Yogyakarta, Single moving average(SMA) AbstrakProses penggunaan sumber daya perguruan tinggi setiap tahun dipengaruhi oleh naik turunnya jumlah mahasiswa. Tujuan dari penelitian ini adalah untuk memprediksi jumlah mahasiswa yang kuliah di jurusan teknik informatika UPN Veteran Yogyakarta untuk periode yang akan datang. Data penelitian ini diambil dari forlap dikti untuk Teknik Informatika dari tahun 2009 sampai 2016 UPN Veteran Yogyakarta. Metode yang digunakan untuk melakukan peramalan jumlah mahasiswa adalah metode Moving Average yang tediri dari : Single Moving Average (SMA), Weighted Moving Average (WMA) dan Exponential Moving Average (EMA). Penelitian ini akan menggunkan akurasi peramalan Mean Square Error (MSE), Mean Absolute Error (MAE) dan Mean Absolute Percentage Error (MAPE) untuk memilih model terbaik yang akan digunakan untuk peramalan. Model terbaik yang digunakan untuk peramalan yaitu Weighted Moving Average (WMA) dengan pembobot 1/3 dan panjang rata-rata (n) yang dipakai sebesar 2. Nilai terkecil untuk MSE sebesar 5807,96; nilai terkecil MAE sebesar 55,89 dan nilai terkecil untuk MAPE sebesar 5,24 %. Peramalan untuk jumlah mahasiswa empat semester kedepan setelah semester genap 2016 masing-masing adalah : 902; 901,33; 901,56 dan 901,48. Kata Kunci : Peramalan, UPN Veteran Yogyakarta, Single Moving Average(SMA).

scholarly journals Forecasting daily COVID-19 confirmed, deaths and recovered cases using univariate time series models: A case of Pakistan study

2020 ◽  
Author(s):  
Hasnain Iftikhar ◽  
Moeeba Iftikhar
Keyword(s):  
Time Series ◽  
Policy Making ◽  
Mean Absolute Error ◽  
Moving Average ◽  
Health Sector ◽  
Absolute Error ◽  
Autoregressive Moving Average ◽  
Time Series Models ◽  
Univariate Time Series ◽  
Simple Exponential Smoothing

The increasing confirmed cases and death counts of Coronavirus disease 2019 (COVID-19) in Pakistan has disturbed not only the health sector, but also all other sectors of the country. For precise policy making, accurate and efficient forecasts of confirmed cases and death counts are important. In this work, we used five different univariate time series models including; Autoregressive (AR), Moving Average (MA), Autoregressive Moving Average (ARMA), Nonparametric Autoregressive (NPAR) and Simple Exponential Smoothing (SES) models for forecasting confirmed, death and recovered cases. These models were applied to Pakistan COVID-19 data, covering the period from 10, March to 3, July 2020. To evaluate models accuracy, computed two standard mean errors such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The findings show that the time series models are useful in predicting COVID-19 confirmed, deaths and recovered cases. Furthermore, MA model outperformed the rest of all models for confirmed and deaths counts prediction, while ARMA is second best model. The SES model seems superior to other models for prediction of recovered counts, however MA is competitive. On the basis of best selected models, we forecast form 4th July to 14th August, 2020, which will be helpful for decision making of public health and other sectors of Pakistan.

scholarly journals Random Regression Forest Model using Technical Analysis Variables

2016 ◽  
Vol 5 (3) ◽  
pp. 85-102 ◽  
Author(s):  
Senol Emir ◽  
Hasan Dincer ◽  
Umit Hacioglu ◽  
Serhat Yuksel
Keyword(s):  
Random Forest ◽  
Banking Sector ◽  
Mean Absolute Error ◽  
Moving Average ◽  
Technical Analysis ◽  
Absolute Error ◽  
Important Variable ◽  
Random Regression ◽  
Random Forest Method ◽  
Technical Indicator

The purpose of this study is to explore the importance and ranking of technical analysis variables in Turkish banking sector. Random Forest method is used for determining importance scores of inputs for eight banks in Borsa Istanbul. Then two predictive models utilizing Random Forest (RF) and Artificial Neural Networks (ANN) are built for predicting BIST-100 index and bank closing prices. Results of the models are compared by three metrics namely Mean Absolute Error (MAE), Mean Square Error (MSE), Median Absolute Error (MedAE). Findings show that moving average (MAV-100) is the most important variable for both BIST -100 index and bank closing prices. Therefore, investors should follow this technical indicator with respect to Turkish banks. In addition ANN shows better performance for all metrics.

scholarly journals Rancangan Penjadwalan Produksi Brankas Dengan Metode Heijunka Di PT. Chubb Safes Indonesia

2021 ◽  
Vol 6 (1) ◽  
pp. 9
Author(s):  
Sopian Maulana ◽  
Nunung Nuhasanah
Keyword(s):  
Production Scheduling ◽  
Mean Absolute Error ◽  
Moving Average ◽  
Working Hours ◽  
Absolute Error ◽  
Exponential Smoothing ◽  
Production Plan ◽  
Daily Production ◽  
Demand Prediction ◽  
Single Exponential

<p><strong>PT. Chubb Safes Indonesia (PT. CSI) is a company engaged in manufacturing and producing safety products for the public. In carrying out safe production, there is often a difference between the production plan made by the production department and the actual production done by the operator on the production floor. From the production plan that has been made, on the week-6 to week-8 from PT. CSI is only 76% to 89% of the production plan achieved. PT. This CSI has a FIFO (First In First Out) scheduling system and implements a mixed production system. Based on the problems experienced by PT. CSI, this can be minimized by predicting demand using the single moving average and single exponential smoothing method then the best demand prediction is obtained based on the value of the smallest deviation using the Mean Absolute Error (MAE) method where the results are obtained in the next week or the 11th week. PT. CSI can produce as many as 472 units of safes in one week. And by scheduling production using the heijunka method PT. CSI can perform 472 safe productions within 75 working hours of the available 88 working hours resulting in an efficiency of 15%. Then PT. CSI can increase daily production capacity from 424 units in the week-8 to 554 units of safes or 92 units of safes per day.</strong></p><p><strong><em>Keywords</em></strong> – <em>Demand prediction, Production scheduling, Heijunka method, Single moving average, Single exponential smoothing, Mean absolute error </em></p>

scholarly journals Forecasting Exports and Imports by using Autoregressive (AR) with Seasonal Dummies and Box-Jenkins Approaches: A Case of Pakistan

2020 ◽  
Vol 31 (3) ◽  
pp. 291-301
Author(s):  
Sahir Pervaiz Ghauri ◽  
Rizwan Raheem Ahmed ◽  
Dalia Streimikiene ◽  
Justas Streimikis
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Mean Absolute Error ◽  
Moving Average ◽  
Trade Deficit ◽  
Absolute Error ◽  
Ar Model ◽  
Mean Square ◽  
Auto Regressive

This research aims to evaluate two econometric models to forecast imports and exports for the financial year (FY) 2020. For this purpose, we used the annual exports and imports data of Pakistan from FY2002 to FY2019. Thus, in this regard, we employed, and compared the results of two econometrics models such as Box Jenkins or Autoregressive Integrated Moving Average (ARIMA), and Auto-Regressive (AR) with seasonal dummies. For examining the precision of forecasting, we employed mean absolute error and root mean square error approaches. The findings of Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) reveal that the ARIMA or Box Jenkins approach provides better accuracy of the forecast for the exports as compared to the AR model with dummies. However, Auto-Regressive (AR) model has demonstrated more precision for the imports as compared to the Box Jenkins model. Hence, the projected forecasting for the growth of export is 1.87% for the FY2020 and projected forecasting for the import demonstrates a negative variation of -1.61% for the FY2020. The findings of the undertaken study recommend the policymakers of Pakistan to take corrective measures to increase exports and to prevent the country from the trade deficit. The policymakers of Pakistan should give more incentives to the exporters and decrease the cost of doing business to be more competitive than the regional economies such as India, Bangladesh, and China.

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