Prediksi Kedatangan Turis Menggunakan Algoritma Weighted Exponential Moving Average

Tourism is an important factor for the development of a country. Tourism can be used as a promotion to introduce natural beauty and cultural uniqueness. Government needs to predict how many tourists will come every year to do a planning. Therefore, an application is needed to help to predict the arrival of tourists in each country. In this paper, we use Weighted Exponential Moving Average (WEMA) method to predict the arrival of tourist, tourism expenditure in the country, and departure using data from 2008 to 2018. Error measurement is calculated using the Mean Absolute Percentage Error (MAPE). The result shows that the lowest average MAPE on arrival data with span 2 is at 3.28. The lowest average MAPE on tourism expenditure data with span 2 is at 3.99%. The result shows that the lowest average MAPE on departure data with span 2 is at 3.63%.

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Towards optimal ATM cash replenishment using time series analysis

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-201953 ◽

2021 ◽

pp. 1-13

Author(s):

Muhammad Rafi ◽

Mohammad Taha Wahab ◽

Muhammad Bilal Khan ◽

Hani Raza

Keyword(s):

Time Series ◽

Moving Average ◽

Arima Model ◽

Mean Absolute Percentage Error ◽

Percentage Error ◽

Absolute Percentage Error ◽

Automatic Teller Machine ◽

Proposed Model ◽

Auto Regressive ◽

Specific Amount

Automatic Teller Machine (ATM) are still largely used to dispense cash to the customers. ATM cash replenishment is a process of refilling ATM machine with a specific amount of cash. Due to vacillating users demands and seasonal patterns, it is a very challenging problem for the financial institutions to keep the optimal amount of cash for each ATM. In this paper, we present a time series model based on Auto Regressive Integrated Moving Average (ARIMA) technique called Time Series ARIMA Model for ATM (TASM4ATM). This study used ATM back-end refilling historical data from 6 different financial organizations in Pakistan. There are 2040 distinct ATMs and 18 month of replenishment data from these ATMs are used to train the proposed model. The model is compared with the state-of- the-art models like Recurrent Neural Network (RNN) and Amazon’s DeepAR model. Two approaches are used for forecasting (i) Single ATM and (ii) clusters of ATMs (In which ATMs are clustered with similar cash-demands). The Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE) are used to evaluate the models. The suggested model produces far better forecasting as compared to the models in comparison and produced an average of 7.86/7.99 values for MAPE/SMAPE errors on individual ATMs and average of 6.57/6.64 values for MAPE/SMAPE errors on clusters of ATMs.

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Novel Spatiotemporal Feature Extraction Parallel Deep Neural Network for Forecasting Confirmed Cases of Coronavirus Disease 2019

10.1101/2020.04.30.20086538 ◽

2020 ◽

Author(s):

Chiou-Jye Huang ◽

Yamin Shen ◽

Ping-Huan Kuo ◽

Yung-Hsiang Chen

Keyword(s):

Neural Network ◽

Deep Neural Network ◽

Absolute Error ◽

Mean Absolute Percentage Error ◽

The Other ◽

Percentage Error ◽

Model Assessment ◽

Absolute Percentage Error ◽

Spatiotemporal Feature ◽

The Mean

AbstractThe coronavirus disease 2019 pandemic continues as of March 26 and spread to Europe on approximately February 24. A report from April 29 revealed 1.26 million confirmed cases and 125 928 deaths in Europe. This study proposed a novel deep neural network framework, COVID-19Net, which parallelly combines a convolutional neural network (CNN) and bidirectional gated recurrent units (GRUs). Three European countries with severe outbreaks were studied—Germany, Italy, and Spain—to extract spatiotemporal feature and predict the number of confirmed cases. The prediction results acquired from COVID-19Net were compared to those obtained using a CNN, GRU, and CNN-GRU. The mean absolute error, mean absolute percentage error, and root mean square error, which are commonly used model assessment indices, were used to compare the accuracy of the models. The results verified that COVID-19Net was notably more accurate than the other models. The mean absolute percentage error generated by COVID-19Net was 1.447 for Germany, 1.801 for Italy, and 2.828 for Spain, which were considerably lower than those of the other models. This indicated that the proposed framework can accurately predict the accumulated number of confirmed cases in the three countries and serve as a crucial reference for devising public health strategies.

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Peramalan Jumlah Populasi Sapi Potong di Kalimantan Selatan Menggunakan Metode Moving Average, Exponential Smoothing dan Trend Analysis

Jurnal Teknologi Agro-Industri ◽

10.34128/jtai.v6i1.88 ◽

2019 ◽

Vol 6 (1) ◽

pp. 41

Author(s):

Jaka Darma Jaya

Keyword(s):

Trend Analysis ◽

Mean Squared Error ◽

Moving Average ◽

Exponential Smoothing ◽

Mean Absolute Percentage Error ◽

Percentage Error ◽

Absolute Percentage Error ◽

Polynomial Trend ◽

Squared Error

Perkembangan produksi daging sapi di Indonesia selama 30 tahun terakhir secara umum cenderung meningkat. Kebutuhan daging sapi di Indonesia masih belum bisa dicukupi oleh supply domestik, sehingga diperlukan impor daging sapi dari luar negeri. Diperlukan kajian tentang proyeksi ketersediaan populasi sapi potong di masa mendatang agar diambil kebijakan yang tepat dalam menjaga stabilitas dan keterpenuhan supply daging nasional. Penelitian ini bertujuan untuk melakukan peramalan jumlah populasi sapi potong menggunakan 3 (tiga) metode peramalan yaitu metode moving average, exponential smoothing dan trend analysis. Hasil peramalan ini selanjutnya diukur akurasinya menggunakan MAD (Mean Absolud Deviation), MSE (Mean Squared Error) dan MAPE (Mean Absolute Percentage Error). Proyeksi populasi sapi potong pada tahun 2019 (periode berikutnya) menggunakan 3 metode peramalan adalah: 195.100 (moving average); 218.225 (exponential smooting) dan 262.899 (trend analysis). Pengukuran akurasi menggunakan MAD, MSE dan MAPE menunjukkan bahwa metode peramalan jumlah populasi sapi potong yang paling akurat adalah peramalan menggunakan metode polynomial trend analysis (MAD 14.716,12; MSE 327.282.084,17; dan MAPE 0,09) karena memiliki tingkat kesalahan yang lebih kecil dibandingkan hasil peramalan menggunakan metode moving average dan exponential smoothing.

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Planning Production Capacity Using Time Series Forecasting Method and Linier Programming

Engineering Management Research ◽

10.5539/emr.v7n2p20 ◽

2018 ◽

Vol 7 (2) ◽

pp. 20

Author(s):

M. Tirtana Siregar ◽

S. Pandiangan ◽

Dian Anwar

Keyword(s):

Average Method ◽

Moving Average ◽

Demand Forecasting ◽

Production Capacity ◽

Mean Absolute Percentage Error ◽

Programming Method ◽

Percentage Error ◽

Absolute Percentage Error ◽

Forecasting Method ◽

Planning Production

The objectives of this research is to determine the amount of production planning capacity sow talc products in the future utilizing previous data from january to december in year 2017. This researched considered three forecasting method, there are Weight Moving Average (WMA), Moving Average (MA), and Exponential Smoothing (ES). After calculating the methods, then measuring the error value using a control chart of 3 (three) of these methods. After find the best forecasting method, then do linear programming method to obtain the exact amount of production in further. Based on the data calculated, the method of Average Moving has a size of error value of Mean Absolute Percentage Error of 0.09 or 9%, Weight Moving Average has a size error of Mean Absolute Percentage Error of 0.09 or 9% and with Exponential Method Smoothing has an error value of Mean Absolute Percentage Error of 0.12 or 12%. Moving Average and Weight Moving Average have the same MAPE amount but Weight Moving Average has the smallest amount Mean Absolute Deviation compared to other method which is 262.497 kg. Based on the result, The Weight Moving Average method is the best method as reference for utilizing in demand forecasting next year, because it has the smallest error size and has a Tracking Signal  not exceed the maximum or minimum control limit is ≤ 4. Moreover, after obtained Weight Moving Average method is the best method, then is determine value of planning production capacity in next year using linier programming method. Based on the linier programming calculation, the maximum amount of production in next year by considering the forecasting of raw materials, production volume, material composition, and production time obtained in one (1) working day is 11,217,379 pcs / year, or 934,781 pcs / month of finished product. This paper recommends the company to evaluate the demand forecasting in order to achieve higher business growth.

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APLIKASI PREDIKSI JANGKA PENDEK HARGA BITCOIN MENGGUNAKAN METODE ARIMA

Jurnal Ilmiah Informatika Komputer ◽

10.35760/ik.2020.v25i3.3128 ◽

2020 ◽

Vol 25 (3) ◽

pp. 160-174

Author(s):

Nur Fitrian Bintang Pradana ◽

Sri Lestanti

Keyword(s):

Moving Average ◽

Mean Absolute Percentage Error ◽

Percentage Error ◽

Autoregressive Integrated Moving Average ◽

Absolute Percentage Error

Bitcoin merupakan mata uang digital yang sekarang paling banyak digunakan. Perubahan harga yang sewaktu-waktu dapat berubah membuat pengguna bitcoin harus teliti ketika melakukan penukaran. Kepopuleran bitcoin terus meningkat dan menjadi aset untuk investasi bagi para penggunanya. Untuk mengatasi perubahan harga yang tidak menentu maka dibutuhkan sebuah aplikasi prediksi harga bitcoin untuk membantu para penggunanya dalam memprediksi harga bitcoin kedepannya. Prediksi dilakukan dengan menggunakan metode Autoregressive Integrated Moving Average (ARIMA) yang mampu menghasilkan tingkat akurasi tinggi dalam prediksi jangka pendek. Metode ini mengabaikan variabel independen dalam membuat prediksi, sehingga cocok untuk data statistik saling terhubung serta memiliki beberapa asumsi yang harus dipenuhi seperti autokorelasi, trend, maupun musiman. Evaluasi hasil prediksi menggunakan Mean Absolute Percentage Error (MAPE). Hasil pengujian menujukkan bahwa model ARIMA (3,1,3) menghasilkan prediksi dengan nilai MAPE terkecil daripada kandidat model lainnya. Rata-rata nilai MAPE yang dihasilkan adalah sebesar 0,84 dan rentang nilai 1,34 untuk prediksi hari pertama dan 0,98 untuk prediksi hari ketujuh. Dengan demikian model ARIMA (3,1,3) mampu menghasilkan prediksi dengan akurasi yang baik dan layak untuk digunakan sebagai metode prediksi bitcoin untuk satu sampai tujuh hari kedepan.

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Estimating Water Reflectance at Near-Infrared Wavelengths for Turbid Water Atmospheric Correction: A Preliminary Study for GOCI-II

Remote Sensing ◽

10.3390/rs12223791 ◽

2020 ◽

Vol 12 (22) ◽

pp. 3791

Author(s):

Jae-Hyun Ahn ◽

Young-Je Park

Keyword(s):

Near Infrared ◽

Accurate Result ◽

Atmospheric Correction ◽

Mean Absolute Percentage Error ◽

Percentage Error ◽

Absolute Percentage Error ◽

Turbid Waters ◽

The Mean ◽

Relationship Of ◽

Spectral Relationship

Atmospheric correction is a fundamental process to remove the atmospheric effect from the top-of-atmosphere level. The atmospheric correction algorithm developed by the Korea Institute of Ocean Science and Technology employs a near-infrared (NIR) water reflectance model to deal with non-negligible NIR water reflectance over turbid waters. This paper describes the NIR water reflectance models using visible bands of the Second Geostationary Ocean Color Imager (GOCI-II). Whereas the previous GOCI uses the 660 nm band to estimate NIR water reflectance (SR660), GOCI-II uses additional 620 and 709 nm bands, which improves estimation of NIR water reflectance. We developed two reflectance models with the additional bands based on a spectral relationship of water reflectance (SR709) and a spectral relationship of inherent optical properties (SRIOP) from red to NIR wavelengths. A preliminary validation of these two reflectance models was performed using both simulations and an in situ dataset. The validation result showed that the mean absolute percentage error of the SR709 model compared with SR660 was reduced by approximately 6% and 10% at 745 and 865 nm, respectively. Moreover, the mean absolute percentage error of the SRIOP model compared with SR660 was reduced by approximately 12% and 16% at 745 and 865 nm, respectively. Note that SR709 produces the most accurate result when there is only one sediment type, and SRIOP shows the most accurate result when various sediment types exist. Users will be able to optionally select the appropriate NIR water reflectance models in the GOCI-II atmospheric correction process to enhance the accuracy of aerosol reflectance correction over turbid waters.

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Fuzzy Inference System Mamdani dan the Mean Absolute Percentage Error (MAPE) untuk Prediksi Permintaan Dompet Pulsa pada XL Axiata Depok

Jurnal Informatika Universitas Pamulang ◽

10.32493/informatika.v2i2.1511 ◽

2017 ◽

Vol 2 (2) ◽

pp. 97

Author(s):

Mochammad Bagoes Satria Junianto

Keyword(s):

Fuzzy Inference System ◽

Fuzzy Inference ◽

Mean Absolute Percentage Error ◽

Percentage Error ◽

Inference System ◽

Absolute Percentage Error ◽

Sample Data ◽

The Mean

Kemajuan perkembangan teknologi informasi pada era globalisasi sekarang ini sangat pesat; hal ini menuntut setiap perusahaan untuk dapat saling bersaing dalam dunia bisnis yang dinamis dan penuh persaingan. Pada proses manjaemen permintaan dompet pulsa di XL Axiata cabang Depok memerlukan peramalan yang cukup matang agar dompet pulsa yang diminta kepada pusat tidak berlebihan atau tidak terlalu sedikit untuk menjaga kestabilan antara penjualan; persediaan dan jumlah permintaan. Untuk dapat melakukan peramalan yang lebih akurat; maka diperlukan suatu metode yang dapat menghitung ketidakpastian yang terjadi; dalam hal ini metode yang digunakan adalah dengan menggunakan Fuzzy inference system metode Mamdani untuk meramalkan jumlah permintaan dompet pulsa berdasarkan jumlah penjualan dan persediaan. Dengan 12 sample data untuk masing-masing sistem satuam yang digunakan hasil yang didapatkan yaitu dengan menggunakan Fuzzy inference system metode mamdani MAPE yang didapat sebesar 18;56% untuk Dompul XL 5k; 5;38% untuk Dompul XL 10k dan 14;2% untuk Dompul XL Rupiah.

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PENERAPAN METODE EXPONENTIAL MOVING AVERAGE PADA PERAMALAN PENGGUNAAN AIR DI PDAM KOTA GORONTALO

Jambura Journal of Probability and Statistics ◽

10.34312/jjps.v1i2.7152 ◽

2020 ◽

Vol 1 (2) ◽

pp. 69-77

Author(s):

WA SALMI ◽

ISMAIL DJAKARIA ◽

RESMAWAN RESMAWAN

Keyword(s):

Water Use ◽

Average Method ◽

Moving Average ◽

Dry Season ◽

Mean Absolute Percentage Error ◽

Percentage Error ◽

Mean Absolute Deviation ◽

Absolute Deviation ◽

Absolute Percentage Error

Facing the dry season, it is probable that there is a lack of water or excess distribution at one point during distribution to every house that uses PDAM water every day. This will result in community instability in using water and inaccurate users. Therefore, forecasting of the amount of water used in PDAM Kota Gorontalo for the next period. The method used to forecast is the Exponential Moving Average method. Criteria in determining the best method is based on the value of Mean Absolute Deviation and Mean Absolute Percentage Error. After forecasting each smoothing constant is compared, the best model. in predicting the amount of water use in PDAM Kota Gorontalo is an Exponential Moving Average with a smoothing constant of 0.15 because it has the smallest MAD and MAPE values.

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SISTEM PENDUKUNG KEPUTUSAN DALAM PEMILIHAN MODEL DENGAN MENGGUNAKAN METODE TIME SERIES

NJCA (Nusantara Journal of Computers and Its Applications) ◽

10.36564/njca.v2i2.29 ◽

2018 ◽

Vol 2 (1) ◽

Author(s):

Noer Chamid ◽

Muhammad Ainul Yaqin ◽

Nailul Izzah

Keyword(s):

Time Series ◽

Moving Average ◽

Mean Absolute Percentage Error ◽

Percentage Error ◽

Mean Absolute Deviation ◽

Absolute Deviation ◽

Trend Line ◽

Absolute Percentage Error

Analisis time series antara lain memahami dan menjelaskan mekanisme tertentu, meramalkan suatu nilai di masa depan dan mengoptimalkan sistem kendali. Dalam pengambilan keputusan yang menggunakan analisis time series tersebut perlu menggunakan software yang prabayar seperti Minitab, SPSS dan SAS sehingga perlu pembuatan sistem informasi yang mendukung keputusan dalam analisis tersebut. Sistem informasi yang dibuat tersebut akan dilakukan uji coba terhadap kehandalan dan diimplementasikan dalam pengambilan keputusan untuk menentukan penyusunan target pendapatan asli daerah di pemerintah daerah atau data lainnya. Model yang digunakan dalam menduga adalah dengan menggunakan 4 (empat) metode, yaitu : Metode Moving Average, Metode Eksponential Smooting, Metode Linier Trend Line dan Seasonal Adjusment. Dari 4 (empat) metode tersebut, dapat dipilih model yang terbaik dengan menggunakan kriteria menentukan nilai Mean Absolute Deviation (MAD) dan Mean Absolute Percentage Error (MAPE) yang terkecil. Sistem informasi yang dibuat tersebut sudah dilakukan uji coba terhadap kehandalan dan diimplementasikan dalam pengambilan keputusan untuk menentukan penyusunan target pendapatan asli daerah di pemerintah daerah. Sistem Pendukung Keputusan ini dapat dijadikan sebagai tool dalam membuat rekomendasi sebuah keputusan.Kata Kunci: Time Series, Sistem Pendukung Keputusan, Pendapatan Asli Daerah

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Modeling of Fixed Joints Stiffness Based on Modified Least-Square

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.536-537.1365 ◽

2014 ◽

Vol 536-537 ◽

pp. 1365-1368

Author(s):

Ming De Duan ◽

Hao Liang Feng ◽

Kang Hua Liu ◽

Jun Yong Lu

Keyword(s):

Experimental Data ◽

Regression Analysis ◽

Relative Error ◽

Machine Tools ◽

Mean Absolute Percentage Error ◽

Least Square ◽

Percentage Error ◽

Regression Equations ◽

Absolute Percentage Error ◽

The Mean

According to experimental data, the model of fixed Joints stiffness in machine tools was built by least square of relative error. The new regression equations were obtained by regression analysis. Compared to the original equations with Gaussian least-square, the relative error of new regression equations is within 3.5%, which reduces by 12.5% and the mean absolute percentage error (MAPE) decreases by 18.0%, 12.4%and 19.0% respectively.

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