Mean absolute percentage error and bias in economic forecasting

2011 ◽  
Vol 113 (3) ◽  
pp. 259-262 ◽  
Author(s):  
Jordi McKenzie
Keyword(s):  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Economic Forecasting ◽  
Absolute Percentage Error

Towards optimal ATM cash replenishment using time series analysis

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.

scholarly journals Penerapan metode fuzzy sugeno untuk prediksi persediaan bahan baku

2019 ◽  
Vol 9 (2) ◽  
pp. 12-20
Author(s):  
Julio Warmansyah ◽  
Dida Hilpiah
Keyword(s):  
Fuzzy Logic ◽  
Raw Materials ◽  
Mean Absolute Percentage Error ◽  
Raw Material ◽  
Percentage Error ◽  
Human Power ◽  
Absolute Percentage Error ◽  
Input Variables ◽  
A Company

 PT. Cahaya Boxindo Prasetya is a company engaged in the manufacture of carton boxes or boxes. The company's activities also include cutting and printing services using machinery and human power. The problem faced in this company is the difficulty of predicting the amount of inventory of raw materials that will be  included in the production. The remaining raw materials for production will be used as the final stock to get the minimum, the goal is to reduce excess stock Overcoming this problem, fuzzy logic is used to predict raw material inventories by focusing on the final stock. In this study using Fuzzy Sugeno, with three input variables, namely: initial inventory, purchase, production, while the output is the final stock. Determination of prediction results using defuzzification using the average concept of MAPE (Mean Absolute Percentage Error). The results obtained, using the Fuzzy Sugeno method can predict the inventory of raw materials with a MAPE value of 38%. 

scholarly journals PERBANDINGAN METODE EARNED VALUE, EARNED SCHEDULE, DAN KALMAN FILTER EARNED VALUE UNTUK PREDIKSI DURASI PROYEK

2020 ◽  
Vol 3 (1) ◽  
pp. 155
Author(s):  
Andree Sugiyanto ◽  
Onnyxiforus Gondokusumo
Keyword(s):  
Kalman Filter ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Project Schedule ◽  
Probabilistic Forecasting ◽  
Earned Value ◽  
Control Data ◽  
Absolute Percentage Error ◽  
Forecasting Method ◽  
Duration Prediction

In the world of construction, control is needed at the implementation stage, which is prediction or forecasting duration project schedule. Estimated project schedule is an important part for project management making decisions that affect the future of the project. Forecasting method commonly used by practitioners in this case the construction project contractor in evaluating prediction of duration is deterministic forecasting method Earned Value Method (EVM), Earned Schedule Method (ESM). Kalman Filter Earned Value Method (KEVM) as probabilistic forecasting method is carried out to produce more accurate predictive value. The purpose of this study to compare the accuracy of three methods. This research was conducted by calculating duration of the project from EVM, ESM, and KEVM on maintenance and reconstruction projects of Jakarta-Cikampek and Jakarta-Tangerang toll roads. The data used from the project control data S-curve. The control data is processed with EVM, ESM, KEVM to determine the comparison between three methods of predicting duration. Prediction results of three methods were tested with Mean Absolute Percentage Error (MAPE). The results of this study indicate that KEVM can reduce errors after Kalman Filter is performed on estimated duration using EVM. ESM duration prediction yields the smallest MAPE value of the three methods. AbstrakDalam dunia pembangunan konstruksi dibutuhkan pengendalian pada tahap pelaksanaan yaitu prediksi atau peramalan durasi jadwal proyek. Perkiraan jadwal proyek adalah bagian penting untuk manajemen proyek membuat keputusan yang mempengaruhi masa depan proyek. Metode peramalan yang umum digunakan para praktisi dalam hal ini kontraktor proyek konstruksi dalam mengevaluasi prediksi durasi adalah metode peramalan deterministik Earned Value Method (EVM), Earned Schedule Method (ESM). Kalman Filter Earned Value Method (KEVM) sebagai metode peramalan probabilistik dilakukan untuk menghasilkan nilai prediksi yang lebih akurat. Tujuan penelitian ini membandingkan akurasi dari ketiga metode. Penelitian ini dilakukan dengan menghitung durasi proyek dari EVM, ESM, dan KEVM pada proyek pemeliharaan dan rekonstruksi jalan tol Jakarta – Cikampek dan Jakarta – Tangerang. Data yang digunakan dari proyek tersebut adalah data-data pengendalian berupa kurva S. Data pengendalian tersebut diolah dengan EVM, ESM, KEVM untuk mengetahui perbandingan antara ketiga metode prediksi durasi tersebut. Hasil prediksi dari ketiga metode diuji dengan Mean Absolute Percentage Error (MAPE). Hasil dari penelitian ini menunjukkan bahwa KEVM dapat mengurangi kesalahan setelah dilakukan Kalman Filter pada perkiraan durasi menggunakan Earned Value Method. Prediksi durasi ESM menghasilkan nilai MAPE yang paling kecil dari ketiga metode.

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

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.

scholarly journals Peramalan Harga Komoditas Dengan Menggunakan Metode Arima-Garch

Jurnal Varian ◽  
2020 ◽  
Vol 3 (2) ◽  
pp. 113-124
Author(s):  
Ulil Azmi ◽  
Wawan Hafid Syaifudin
Keyword(s):  
Arima Model ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Data History

Emas, Tembaga dan Minyak merupakan jenis komoditas yang banyak diincar oleh para investor untuk menanamkan modal dengan cara melakukan investasi pada jenis komoditas tersebut. Prediksi harga komoditas sangat bermanfaat bagi investor untuk melihat prospek investasi komoditas pada suatu perusahaan di masa yang akan datang. Harga komoditas memiliki karakteristik data yang tidak stabil atau sering disebut volatilitas. Untuk mengatasi permasalahan tersebut, dilakukan peramalan dengan metode ARIMA dan ARIMA-GARCH. Dipilih dua metode tersebut karena dua metode ini cocok untuk meramalkan sesuatu yang memiliki data history yang kuat. Metode ARIMA ARCH-GARCH lebih cocok digunakan untuk data-data yang memliki volatilitas yang tinggi atau terdapat heteroskedastisitas pada residual data, sehingga hasil prediksi lebih akurat. Hal ini dibuktikan dengan nilai AIC lebih kecil dari pada hanya menggunakan metode ARIMA. Model terbaik untuk komoditas Emas adalah ARIMA(0,1,1) – GARCH(1,1) sedangkan komoditas tembaga memiliki model terbaik yaitu ARIMA(2,1,2) – GARCH(1,1) dan komoditas minyak yaitu ARIMA(1,1,1) – GARCH(0,1). Nilai MAPE (Mean Absolute Percentage Error) untuk masing-masing komoditas berturut-turut adalah 1,113; 0,542 dan 1,158 untuk Emas, Tembaga dan Minyak.

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

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.

scholarly journals Planning Production Capacity Using Time Series Forecasting Method and Linier Programming

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.

APLIKASI PREDIKSI JANGKA PENDEK HARGA BITCOIN MENGGUNAKAN METODE ARIMA

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.

Prediksi Kedatangan Turis Menggunakan Algoritma Weighted Exponential Moving Average

2020 ◽  
Vol 12 (2) ◽  
pp. 129-132
Author(s):  
Sherly Florencia ◽  
Alethea Suryadibrata
Keyword(s):  
Moving Average ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Error Measurement ◽  
Absolute Percentage Error ◽  
Natural Beauty ◽  
Expenditure Data ◽  
The Mean ◽  
Tourism Expenditure ◽  
Using Data

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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