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 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 Daily Forecasting for Antam's Certified Gold Bullion Prices in 2018-2020 using Polynomial Regression and Double Exponential Smoothing

2021 ◽  
Vol 3 (4) ◽  
pp. 45-53
Author(s):  
Tresna Maulana Fahrudin ◽  
Prismahardi Aji Riyantoko ◽  
Kartika Maulida Hindrayani ◽  
I Gede Susrama Mas Diyasa
Keyword(s):  
Time Series Data ◽  
Polynomial Regression ◽  
Exponential Smoothing ◽  
Series Data ◽  
Percentage Error ◽  
Training Set ◽  
Double Exponential ◽  
Forecasting Performance ◽  
One Step ◽  
Double Exponential Smoothing

Gold investment is currently a trend in society, especially the millennial generation. Gold investment for the younger generation is an advantage for the future. Gold bullion is often used as a promising investment, on other hand, the digital gold is available which it is stored online on the gold trading platform. However, any investment certainly has risks, and the price of gold bullion fluctuates from day to day. People who invest in gold hopes to benefit from the initial purchase price even if they must wait up to five years. The problem is how they can notice the best time to sell and buy gold. Therefore, this research proposes a forecasting approach based on time series data and the selling of gold bullion prices per gram in Indonesia. The experiment reported that Holt’s double exponential smoothing provided better forecasting performance than polynomial regression. Holt’s double exponential smoothing reached the minimum of Mean Absolute Percentage Error (MAPE) 0.056% in the training set, 0.047% in one-step testing, and 0.898% in multi-step testing.

scholarly journals Peramalan Gelombang Covid 19 Menggunakan Hybrid Nonlinear Regression Logistic – Double Exponential Smoothing di Indonesia dan Prancis

2021 ◽  
Vol 3 (1) ◽  
pp. 37-51
Author(s):  
I Gusti Bagus Ngurah Diksa
Keyword(s):  
Time Series ◽  
Nonlinear Regression ◽  
Forecast Accuracy ◽  
Exponential Smoothing ◽  
Forecasting Model ◽  
Double Exponential ◽  
Single Method ◽  
Double Exponential Smoothing ◽  
The Government ◽  
Second Wave

ABSTRAKIndonesia dan Prancis adalah dua Negara yang mengalami Covid 19 dengan pola pergerakan kasus Covid 19 yang berbeda. Kondisi Indonesia masih mengalami siklus one wave namun Prancis sudah masuk pada pola second wave. Makna second wave adalah kondisi epidemi Covid 19 yang baru muncul setelah epidemi sebelumnya dianggap selesai. Dalam peramalan kasus Covid 19 baik itu terkait informasi puncak dari terjadinya kasus Covid 19 serta ramalan terkait akan berakhirnya pandemi kasus Covid 19 suatu negara merupakan hal penting bagi pemerintah suatu Negara. Model hybrid meningkatkan akurasi ramalan dibandingkan model time series yang dilakukan secara terpisah. Tujuan penelitian ini adalah melakukan peramalan kasus Covid 19 di Indonesia dan Prancis dengan menggunakan metode hybrid dan membandingkan dengan peramalan dengan salah satu metode tunggal. Metode yang digunakan adalah metode tunggal yaitu Nonlinear Regression Logistic dan metode Hybrid Nonlinear Regression Logistic–Double Eksponensial Smoothing. Hasilnya adalah model peramalan Hybrid Nonlinear Regression Logistic and Doubel Exponential Smoothing lebih bagus digunakan dalam peramalan kasus Covid 19 di Indonesia dan Prancis. Terlihat bahwa nilai MAPE model Hybrid Nonlinear Regression Logistic–Double Eksponensial Smoothing jauh lebih kecil dibandingkan model peramalan Nonlinear Regression Logistic. ABSTRACTIndonesia and France are two countries that have experienced Covid 19 with different patterns of movement of Covid 19 cases. Indonesia's condition is still experiencing a one wave cycle but France has entered into the second wave pattern. The meaning of the second wave is the condition of the Covid 19 epidemic which only emerged after the previous epidemic was considered over. In forecasting the Covid 19 case, whether it is related to the peak information on the occurrence of the Covid 19 case and predictions regarding the end of the pandemic of the Covid 19 case in a country, it is important for the government of a country. The hybrid model improves forecast accuracy compared to the time series model which is carried out separately. The purpose of this study is to forecast the cases of Covid 19 in Indonesia and France using the hybrid method and comparing with forecasting with one single method. The method used is a single method, namely Nonlinear Logistic Regression and Hybrid Nonlinear Regression Logistic-Double Exponential Smoothing methods. The result is that the Hybrid Nonlinear Regression Logistic and Double Exponential Smoothing forecasting model is better used in forecasting the Covid 19 cases in Indonesia and France. It can be seen that the MAPE value of the Hybrid Nonlinear Regression Logistic – Double Exponential Smoothing model is much smaller than the Nonlinear Regression Logistic forecasting model.

Implementasi Metode Double Exponential Smoothing dalam Memprediksi Pertambahan Jumlah Penduduk di Wilayah Kabupaten Karawang

2020 ◽  
Vol 12 (2) ◽  
pp. 95-103
Author(s):  
Andini Diyah Pramesti ◽  
Mohamad Jajuli ◽  
Betha Nurina Sari
Keyword(s):  
Population Distribution ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Percentage Error ◽  
Population Census ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Exponential Smoothing Method ◽  
Better Than ◽  
Population Numbers

The density and uneven distribution of the population in each area must be considered because it will cause problems such as the emergence of uninhabitable slums, environmental degradation, security disturbances, and other population problems. In the data obtained from the 2010 population census based on the level of population distribution in Karawang District, the area of West Karawang, East Karawang, Rengasdengklok, Telukjambe Timur, Klari, Cikampek and Kotabaru are zone 1 regions which are the densest zone with a population of 76,337 people up to 155,471 inhabitants. This research predicts / forecasting population growth in the 7 most populated areas for the next 1 year using Double Exponential Smoothing Brown and Holt methods. This study uses Mean Absolute Percentage Error (MAPE) to evaluate the performance of the double exponential smoothing method in predicting per-additional population numbers. Forecasting results from the two methods place the Districts of East Telukjambe, Cikampek, Kotabaru, East Karawang, and Rengasdengklok in 2020 to remain in zone 1 with a range of 76,337 people to 155,471 inhabitants. Whereas in the Districts of Klari and West Karawang are outside the range in zone 1 because both districts have more population than the range in zone 1. From the results of MAPE both methods are found that 6 out of 7 districts in the method Holt's double exponential smoothing produces a smaller MAPE value compared to the MAPE value generated from Brown's double exponential smoothing method. It was concluded that in this study the Holt double exponential smoothing method was better than Brown's double exponential smoothing method.

scholarly journals PERBANDINGAN METODE FUZZY TIME SERIES DAN HOLT DOUBLE EXPONENTIAL SMOOTHING PADA PERAMALAN JUMLAH MAHASISWA BARU INSTITUT PERTANIAN BOGOR

2013 ◽  
Vol 12 (2) ◽  
pp. 25
Author(s):  
S. STEVEN ◽  
S. NURDIATI ◽  
F. BUKHARI
Keyword(s):  
Time Series ◽  
Exponential Smoothing ◽  
Mean Absolute Percentage Error ◽  
Fuzzy Time Series ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Double Exponential ◽  
Double Exponential Smoothing

Peramalan merupakan kegiatan memprediksi nilai suatu variabel di masa yang akan datang. Tujuan penelitian ini adalah memprediksi jumlah mahasiswa baru Institut Pertanian Bogor dengan menggunakan metode fuzzy time series dan metode pemulusan eksponensial ganda dari Holt serta membandingkan kedua metode tersebut dengan cara melihat tingkat ketepatan peramalan Mean Absolute Percentage Error (MAPE). Metode fuzzy time series menggunakan himpunan fuzzy dalam proses peramalannya sedangkan metode pemulusan eksponensial ganda dari Holt menggunakan pemulusan nilai dari serentetan data dengan cara menguranginya secara eksponensial. Dalam meramalkan jumlah mahasiswa baru Institut Pertanian Bogor, metode fuzzy time series menghasilkan tingkat ketepatan peramalan yang lebih baik dengan nilai MAPE sebesar 6.41 % dibandingkan dengan metode pemulusan eksponensial ganda dari Holt dengan nilai MAPE sebesar 7.75 %. Setelah dilakukan studi kasus, metode pemulusan eksponensial ganda dari Holt akan lebih akurat hasil peramalannya jika data yang digunakan lebih banyak.

scholarly journals Sistem Informasi Peramalan Penjualan Barang Dengan Metode Double Exponential Smoothing (Studi Kasus Istana Sayur)

2020 ◽  
Vol 6 (3) ◽  
pp. 9-14
Author(s):  
Yuri Ariyanto ◽  
Ahmadi Yuli Ananta ◽  
Muhammad Robbi Darwis Darwis
Keyword(s):  
Forecast Error ◽  
Exponential Smoothing ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Double Exponential ◽  
Double Exponential Smoothing

Abstrak—Istana Sayur merupakan salah satu toko yang menjual beberapa macam sayuran, buah buahan dan bahan makanan yang selalu berusaha meningkatkan dan menjaga kualitas layanan, mencoba mengurangi kerugian dari pengendalian persediaan stok barang secara manual yang kurang baik akibat kelebihan dan kekurangan stok yang dialami saat ini, maka diperlukan fitur sebagai sistem informasi kasir dan peramalan stok barang. Tujuan dari pembuatan sistem informasi ini adalah analisa Forecasting secara manual ke dalam sebuah sistem informasi agar lebih praktis, dengan pemrograman PHP berframework CodeIgniter dan MySQL sebagai databasenya. Dengan menggunakan metode Double Exponential Smoothing Holt untuk pengambilan keputusan dalam jangka waktu tertentu dan pemanfaatkan pergerakan data pada masa lalu yang bersifat trend dimana datanya bersifat linier. Setelah dilakukan observasi pada Istana Sayur, Malang, didapat data transaksi penjualan dan barang pada tahun 2016-2018. Dari hasil perhitungan metode yang dipakai pada sistem ini kemudian dihitung Forecast Error-nya dengan menggunakan metode Mean Absolute Percentage Error. Dari analisa yang telah dilakukan, didapatkan hasil bahwa dengan menggunakan Mean Absolute Percentage Error didapat nilai untuk Sawi Caisim Manis dengan nilai 15.05%, Telor Ayam dengan nilai 15.78%, Cabe Hijau dengan nilai 12.45%, Buncis dengan nilai 22.22%, Cengkeh dengan nilai 34.69%, Bawang Putih dengan nilai 19.53%, Tempe dengan nilai 20.60% dan Kentang dengan nilai 17.58%. Sehingga Sawi Caisim Manis, Telor Ayam, Cabe Hijau, Bawang Putih dan Kentang tergolong kedalam kategori baik karena memiliki nilai diantara 10%-20%. Sedangkan untuk Buncis, Cengkeh dan Tempe tergolong kedalam kategori cukup karena memiliki nilai diantara 20%-50%. Saran untuk pengembangan aplikasi ini adalah perlunya penambahan metode lain sebagai pembanding tingkat keakuratan.

scholarly journals Application of Artificial Intelligence in Prediction of Road Freight Transportation

2017 ◽  
Vol 29 (4) ◽  
pp. 363-370 ◽  
Author(s):  
Bogna Mrowczynska ◽  
Maria Ciesla ◽  
Aleksander Krol ◽  
Aleksander Sladkowski
Keyword(s):  
Artificial Intelligence ◽  
Historical Data ◽  
Percentage Error ◽  
Double Exponential ◽  
Road Freight ◽  
The Mean ◽  
Double Exponential Smoothing ◽  
Trucking Companies ◽  
Road Freight Transportation

Road freight transport often requires the prediction of volume. Such knowledge is necessary to capture trends in the industry and support decision making by large and small trucking companies. The aim of the presented work is to demonstrate that application of some artificial intelligence methods can improve the accuracy of the forecasts. The first method employed was double exponential smoothing. The modification of this method has been proposed. Not only the parameters but also the initial values were set in order to minimize the mean absolute percentage error (MAPE) using the artificial immune system. This change resulted in a marked improvement in the effects of minimization, and suggests that the variability of the initial value of S2 has an impact on this result. Then, the forecasting Bayesian networks method was applied. The Bayesian network approach is able to take into account not only the historical data concerning the volume of freight, but also the data related to the overall state of the national economy. This significantly improves the quality of forecasting. The application of this approach can also help in predicting the trend changes caused by overall state of economy, which is rather impossible when analysing only the historical data.

scholarly journals Penerapan Model Exponential Smoothing berbasis Metode Evolutionary pada Kasus COVID-19 dan DBD di Bojonegoro

2021 ◽  
Vol 6 (3) ◽  
pp. 174
Author(s):  
Denny Nurdiansyah ◽  
Khoirul Wafa
Keyword(s):  
Mean Squared Error ◽  
Visual Basic ◽  
Exponential Smoothing ◽  
Percentage Error ◽  
Absolute Deviation ◽  
Squared Error ◽  
Double Exponential ◽  
Reduced Gradient ◽  
Double Exponential Smoothing ◽  
Generalized Reduced Gradient

Latar Belakang: COVID-19 menjadi perhatian utama di Bojonegoro karena kasus terinfeksi meningkat sampai akhir tahun 2020. Selain itu, wabah demam berdarah dengue (DBD) juga perlu diantisipasi di musim penghujan agar tidak meningkat bersamaan dengan wabah COVID-19.Tujuan: Mengembangkan model exponential smoothing berbasis metode evolutionary untuk meramalkan banyaknya kasus terinfeksi COVID-19 dan DBD di Bojonegoro.Metode: Penelitian diawali dengan pembuatan aplikasi peramalan model exponential smoothing dengan metode evolutionary dan pemrograman Visual Basic yang dikembangkan di Excel dan Solver. Koefisien-koefisien model dioptimasi secara iteratif dengan metode evolutionary dan metode generalized reduced gradient. Model tersebut dievaluasi kinerjanya dengan nilai mean absolute percentage error (MAPE), mean absolute deviation (MAD), dan mean squared error (MSE). Sumber data penelitian menggunakan data sekunder dari Dinas Kesehatan Bojonegoro yang berisi data harian kasus terinfeksi COVID-19 dan data bulanan kasus DBD.Hasil: Model double exponential smoothing berbasis metode generalized reduced gradientmenghasilkan kesalahan model peramalan yang lebih kecil untuk nilai MAPE, MAD, dan MSE. Hasil peramalan menunjukkan bahwapeningkatan terjadi pada periode ke depan untuk kasus terinfeksi COVID-19 yang lebih besar dibandingkan DBD.Kesimpulan: Aplikasi peramalan model exponential smoothing dapat menjadi altenatif dalam meramalkan banyaknya kasus terinfeksi COVID-19 dan DBD di Bojonegoro.

scholarly journals Aplikasi Peramalan Jumlah Siswa Sekolah Dasar di Kabupaten Tanah Laut Menggunakan Metode Holt’s Double Exponential Smoothing

2018 ◽  
Vol 7 (2) ◽  
pp. 64-69
Author(s):  
Nora Apriliyani ◽  
Herfia Rhomadhona ◽  
Jaka Permadi
Keyword(s):  
Exponential Smoothing ◽  
Learning Processes ◽  
Flow Diagram ◽  
Percentage Error ◽  
School Students ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Entity Relationship ◽  
Entity Relationship Diagram ◽  
Exponential Smoothing Method

Learning processes in the elementary schools at Tanah Laut District is affected by the number of students. Through the number of students can be predicted how much the need of additional teachers, rooms, textbooks and learning medias that support learning processes in the schools. In other words, the infrastructure of the schools can be predicted by the number of students that is registered in Tanah Laut District. The research use Holt’s Double Exponential Smoothing method to predicting the number of the prospective students in Tanah Laut District. Mean Absolute Percentage Error (MAPE) technique is used to calculate the percentage of error from the forecasting’s result. The system is designed by Entity Relationship Diagram (ERD) and Data Flow Diagram (DFD). The forecasting that have been done said that the number of Tanah Laut’s elementary school students at 2018 is 35655 students, with the value of MAPE is about 0.77%, a = 0.77 and ß = 0.8.

scholarly journals Prediksi Penjualan Kertas Menggunakan Metode Double Exponential Smoothing

2021 ◽  
Vol 9 (1) ◽  
pp. 60
Author(s):  
Erinsyah Aditya Nugroho Putro ◽  
Elistya Rimawati ◽  
Retno Tri Vulandari
Keyword(s):  
Raw Materials ◽  
Exponential Smoothing ◽  
Economic Losses ◽  
Percentage Error ◽  
Business Owner ◽  
Know How ◽  
Goods And Services ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Exponential Smoothing Method

One of the important thing in business is the inventory of goods and services. Business goal can be reached when business owner know how the number of their inventory. Printing business is using forecasting model in their purchasing raw materials to estimate and calculate their selling prediction. That model is used to minimize economic losses when the costumer canceled order because paper was ran out and to prevent paper damage does not occur date to storage that to long. Double Exponential Smoothing method is used in this research to predict the sales of Paper A and HVS A3+ paper and calculates the prediction error with MAPE (Mean Absolute Percentage Error). This study aims to make an accurate forecasting application. The prediction results from application are in the form of prediction calculations for sales in the following month which will be used to optimize the purchase of paper to be sold. In applying the research results of Paper A and HVS A3 +, the best alpha was obtained in the 12th period, namely 0.3 and 0.6 with a MAPE error of 12% and 18% and an accuracy rate of 88% and 82% where the alpha was used to predict period 13 and produces a forecast value of 446 for Paper A and 474 for HVS A3 +

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