Forecasting Error Calculation with Mean Absolute Deviation and Mean 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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Aplikasi Triple Exponential Smoothing Untuk Forecasting Jumlah Penduduk Miskin

Jurnal Derivat: Jurnal Matematika dan Pendidikan Matematika ◽

10.31316/j.derivat.v3i2.719 ◽

2020 ◽

Vol 3 (2) ◽

pp. 76-82

Author(s):

Padrul Jana

Keyword(s):

Poverty Reduction ◽

Mean Squared Error ◽

Exponential Smoothing ◽

Mean Absolute Percentage Error ◽

Percentage Error ◽

Poor People ◽

Mean Absolute Deviation ◽

Absolute Deviation ◽

Absolute Percentage Error ◽

The Government

This study aims to predict the number of poor in Indonesia for the next few years using a triple exponential smoothing method.The purpose of this research is the result of the forecast number of poor people in Indonesia accurate forecast results are used as an alternative data the government for consideration of government to determine the direction of national poverty reduction policies. This research includes the study of literature research, by applying the theory of forecasting to generate predictions of poor people for coming year. Furthermore, analyzing the mistakes of the methods used in terms of the count: Mean Absolute Deviation (MAD), Mean Square Error (MSE), Mean absolute percentage error (MAPE) and Mean Percentage Error (MPE). The function of this error analysis is to measure the accuracy of forecasting results that have been conducted.These results indicate that the number of poor people in 2017 amounted to 24,741,871 inhabitants, in 2018 amounted to 24,702,928 inhabitants, in 2019 amounted to 24,638,022 inhabitants and in 2020 amounted to 24,547,155 people. The forecasting results show an average reduction in the number of poor people in Indonesia last five years (2016-2020 years) ranges from 0.16 million. Analysis forecasting model obtained an mean absolute deviation (MAD) obtained by 0.246047. Mean squared error (MSE) of forecasting results with the original data by 1.693277. Mean absolute percentage error (MAPE) of 3.040307% and the final Mean percentage error (MPE) of 0.888134%.Kata Kunci: Forecasting, Triple Exponential Smoothing

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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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PENERAPAN METODE HOLT WINTER DAN SEASONAL ARIMA PADA PERAMALAN PERKEMBANGAN WISATAWAN MANCANEGARA YANG DATANG KE INDONESIA.

Jurnal Matematika UNAND ◽

10.25077/jmu.6.4.29-36.2017 ◽

2017 ◽

Vol 6 (4) ◽

pp. 29

Author(s):

Dila Mulya ◽

Yudiantri Asdi ◽

Ferra Yanuar

Keyword(s):

Mean Absolute Percentage Error ◽

Percentage Error ◽

Mean Absolute Deviation ◽

Absolute Deviation ◽

Absolute Percentage Error

Abstrak. Pada tugas akhir ini akan dirumuskan pemodelan peramalan perkembanganwisatawan mancanegara yang datang ke Indonesia dengan metode Holt Winter dan Sea-sonal ARIMA. Kemudian hasil peramalan perkembangan wisatawan dengan menggu-nakan kedua metode tersebut akan dibandingkan berdasarkan nilai Mean Squared Devi-ation (MSD), Mean Absolute Percentage Error (MAPE) serta Mean Absolute Deviation(MAD). Berdasarkan hasil yang diperoleh, model terbaik untuk peramalan perkem-bangan wisatawan mancanegara yang datang ke Indonesia adalah model SARIMA(0; 1; 1)(1; 1; 0)12 , karena nilai MAPE, MAD dan MSD yang diperoleh lebih kecil dari-pada model Holt Winter.Kata Kunci: Holt Winter, Seasonal Arima, Trend, Musiman

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COMPARISON OF SOME STATISTICAL FORECASTING TECHNIQUES WITH GMDH PREDICTOR: A CASE STUDY

Journal of Mechanical Engineering ◽

10.3329/jme.v47i1.35354 ◽

2018 ◽

Vol 47 (1) ◽

pp. 16-21 ◽

Cited By ~ 1

Author(s):

Syed Misbah Uddin ◽

Aminur Rahman ◽

Emtiaz Uddin Ansari

Keyword(s):

Time Series ◽

Mean Square Error ◽

Moving Average ◽

Exponential Smoothing ◽

Mean Absolute Percentage Error ◽

Percentage Error ◽

Mean Absolute Deviation ◽

Mean Square ◽

Absolute Deviation ◽

Absolute Percentage Error

Demand forecasts are extremely important for manufacturing industry and also needed for all type of business and business suppliers for distribution of finish products to the consumer on time. This study is concerned with the determination of accurate models for forecasting cement demand. In this connection this paper presents results obtained by using a self-organizing model and compares them with those obtained by usual statistical techniques. For this purpose, Monthly sales data of a typical cement ranging from January, 2007 to February, 2016 were collected. A nonlinear modelling technique based on Group Method of Data Handling (GMDH) is considered here to derive forecasts. Forecast were also made by using various time series smoothing techniques such as exponential smoothing, double exponential smoothing, moving average, weightage moving average and regression method. The actual data were compared to the forecast generated by the time series model and GMDH model. The mean absolute deviation (MAD, mean absolute percentage error (MAPE) and mean square error (MSE) were also calculated for comparing the forecasting accuracy. The comparison of modelling results shows that the GMDH model perform better than other statistical models based on terms of mean absolute deviation (MAD), mean absolute percentage error (MAPE) and mean square error (MSE).

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PERBANDINGAN METODE FTS DAN MA PADA PERAMALAN PERSEDIAAN BERAS

Jurnal Informatika Polinema ◽

10.33795/jip.v6i3.314 ◽

2020 ◽

Vol 6 (3) ◽

pp. 29-36

Author(s):

Deddy Kusbianto ◽

Agung Pramudhita ◽

Nurhalimah

Keyword(s):

Time Series ◽

Mean Square Error ◽

Moving Average ◽

Mean Absolute Percentage Error ◽

Fuzzy Time Series ◽

Percentage Error ◽

Mean Absolute Deviation ◽

Mean Square ◽

Absolute Deviation ◽

Absolute Percentage Error

Dalam memenuhi kebutuhan masyarakat Kabupaten Malang dan menjaga stabilitas ketersediaan beras pemerintah setempat perlu melakukan proses peramalan. Dimana dalam melakukan proses peramalan menggunakan metode peramalan, salah satunya dengan menggunakan metode Fuzzy Time Series dan Moving Average yaitu dengan menangkap pola dari data yang telah lalu kemudian digunakan untuk memproyeksikan data yang akan da¬¬tang. Dari hasil implementasi dua metode tersebut menghasilkan perbandingan jumlah persediaan beras. hasil perbandingan tersebut akan dipakai untuk mengukur tingkat error dari masing – masing metode dengan menggunakan MAD (Mean Absolute Deviation), MSE (Mean Square Error), RMSE ( Root Square Error ) dan MAPE (Mean Absolute Percentage Error). Kesimpulannya adalah metode fuzzy time series cocok digunakan untuk studi kasus peramalan persediaan beras dibandingkan menggunakan metode moving average. Sehingga untuk proses peramalan selanjutnya dan untuk mendapatkan hasil dengan tingkat error sedikit dapat menggunakan metode fuzzy time series

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ARDL METHOD: Forecasting Data Curah Hujan Harian NTB

Jurnal Varian ◽

10.30812/varian.v3i2.627 ◽

2020 ◽

Vol 3 (2) ◽

pp. 73-82

Author(s):

Ulul Azmi ◽

Zilullah Nazir Hadi ◽

Siti Soraya

Keyword(s):

Mean Squared Error ◽

Percentage Error ◽

Mean Absolute Deviation ◽

Absolute Deviation ◽

Root Mean Squared Error ◽

Absolute Percentage Error ◽

Distributed Lag ◽

Autoregressive Distributed Lag ◽

Squared Error ◽

Parameter Error

Penelitian ini berisi tentang prediksi atau forecasting data iklim di Nusa Tenggara Barat (NTB) tahun 2011, yakni jumlah hari terjadinya hujan dengan menggunakan metode Autoregressive Distributed Lag (ARDL). Data yang digunakan yaitu data iklim di Nusa Tenggara Barat (NTB) dari tahun 2006 -2010, dengan menggunakan beberapa parameter error seperti Mean Absolute Deviation (MAD), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) dan Mean Absolute Percentage Error (MAPE). Berdasarkan hasil simulasi data iklim di Nusa Tenggara Barat (NTB) tersebut, diperoleh prediksi jumlah hari terjadinya curah hujan pada tahun 2011 sebesar 226 hari dengan nilai MAD 20,8069, MSE 3,5569, RMSE 1,88597, dan MAPE 11,9297 . Dan prediksi jumlah hari terjadinya hujan pada tahun 2011 sebanyak 225,928 hari atau jika di bulatkan menjadi 226 hari dengan nilai parameter error MAD sebesar 20,8069, sehingga dapat disimpulkan pada tahun 2011 terjadi peningkatan jumlah hari terjadinya hujan di Nusa Tenggara Barat (NTB).

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Modelling the Employment in Tourism – Case Study of Croatia

International Journal of Business Administration ◽

10.5430/ijba.v8n3p37 ◽

2017 ◽

Vol 8 (3) ◽

pp. 37 ◽

Cited By ~ 1

Author(s):

Maja Mamula ◽

Kristina Duvnjak

Keyword(s):

Exponential Smoothing ◽

Mean Absolute Percentage Error ◽

Percentage Error ◽

Reliable Estimate ◽

Monthly Data ◽

Absolute Percentage Error ◽

Forecasting Error ◽

Forecasting Methods ◽

Percentage Share

According to the data on the share of employees in the category Hotels and similar accommodation in the total employees (16.6% in 2015), it can be concluded that this percentage share is quite significant. In this paper the number of employees in tourism (in the category Hotels and similar accommodation) is modelled and predicted on the basis of monthly data from the period 2005 to 2015, collected from the First Release of the Croatian Bureau of Statistics. Taking into consideration the seasonal character of the phenomenon being analysed, taking into account the criteria of reliability of demonstrated forecasts, in this study following methods were used: the seasonal naive models, Holt - Winters Model trend seasonality exponential smoothing and Holt- Winters no seasonal exponential smoothing model. All obtained results were compared by forecasting error Mean Absolute Percentage error (MAPE). The obtained results indicate that forecasting methods which take into account the seasonal character of the phenomenon result in smaller forecasting error, and more reliable estimate, compared to models which don´t take into account the character of the phenomenon being analysed.

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IMPLEENTASI MODEL HYBRID DALAM MEMPREDIKSI PENYEBARAN COVID-19 DI WILAYAH JAWA BARAT

SEMINASTIKA ◽

10.47002/seminastika.v3i1.261 ◽

2021 ◽

Vol 3 (1) ◽

pp. 124-137

Author(s):

Tatang Rohana Cucu

Keyword(s):

World Health Organization ◽

Error Rate ◽

World Health ◽

Percentage Error ◽

Mean Absolute Deviation ◽

Absolute Deviation ◽

Data Set ◽

Absolute Percentage Error ◽

Hybrid Data ◽

Health Organization

Di awal tahun 2020, dunia dikagetkan dengan kejadian infeksi berat dengan penyebab yang belum diketahui, yang berawal dari laporan dari Cina kepada World Health Organization (WHO) terdapatnya 44 pasien pneumonia yang berat di suatu wilayah yaitu Kota Wuhan, Provinsi Hubei, China, tepatnya di akhir tahun 2019. Pada perkembangannya, wabah ini kemudian diidentifikasi sebagai wabah Virus Covid-19. Penambahan jumlah kasus COVID-19 berlangsung sangat cepat, sampai dengan 16 Februari 2020, secara global dilaporkan 51.857 kasus terkonfirmasi di 25 negara dengan 1.669 kematian (CFR 3,2%). Di Indonesia ada lebih dari 2 ribu kasus ditemukan dan hampir 200 orang telah meninggal. Di wilayah Jawa barat, kasus positif Covid-19 juga terus bertambah. Data penyebaran virus Corona Covid-19 di Jawa Barat mengalami perubahan, Rabu (1/4/2020). Terpantau melalui situs resmi Pusat Informasi dan Koordinasi Covid-19 Provinsi Jawa Barat (Pikobar), jumlah orang terpapar positif Corona mencapai 198 orang. Data yang diakses dari pikobar.jabarprov.go.id tersebut juga merilis sudah ada 11 pasien yang dinyatakan sembuh dan diperbolehkan pulang dari rumah sakit. Sementara jumlah pasien yang meninggal dunia berjumlah 21 orang. Sedangkan untuk pasien dalam pengawasan (PDP), jumlah yang telah diproses dalam pengawasan mencapai 727 orang. Sedangkan yang telah selesai menjalani pengawasan mencapai 242 orang. Total PDP di Jabar berjumlah 969 orang. Berbekal dari data Pusat Informasi dan Koordinasi Covid-19 Jawa Barat, penulis tertarik melakukan penelitian untuk memprediksi penyebaran kasus positif Covid-19 di Jawa Barat. Dalam penelitian ini, model yang digunakan adalah Hybrid. Data set yang digunakan adalah data pasien positif Covid-19 mulai bulan April 2020 sampai dengan bulan Februari 2021. Berdasarkan hasil penelitian yang sudah dilakukan, model Hybrid mampu memprediksi jumlah penyebaran kasus Covid-19 di Jawa Barat. Hal ini dibuktikan dengan hasil training teknik Hybrid memiliki error rate sebesar 0,4615, yang dilanjutkan dengan analisa akurasi prediksi selama tiga bulan, yaitu bulan Desember 2020, Januari, dan Febrauri 2021. Dari hasil prediksi, model Hybrid memiliki nilai rata – rata Mean Absolute Deviation (MAD) sebesar 351. Sedangkan berdasarkan analisa prediksi dengan teknik Mean Absolute Percentage Error (MAPE) memiliki rata rata tingkat kesalahan sebesar 0,2061 atau 20,6%.

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Previsão dos modelos univariados e rede neural da demanda turística do estado de Mato Grosso do Sul

Caderno Virtual de Turismo ◽

10.18472/cvt.19n3.2019.1520 ◽

2020 ◽

Vol 19 (3) ◽

Cited By ~ 1

Author(s):

Bruno Matos Porto ◽

Daniela Althoff Philippi ◽

Vanessa Aline Wagner Leite

Keyword(s):

Mean Squared Error ◽

Percentage Error ◽

Mean Absolute Deviation ◽

Absolute Deviation ◽

Mato Grosso ◽

Absolute Percentage Error ◽

Squared Error ◽

Mato Grosso Do Sul

O objetivo deste artigo foi gerar previsões de curto, médio e longo prazos e comparar a precisão dos modelos em cada horizonte de previsão. Para atender o objetivo foram aplicados os modelos univariados e rede neural (NNAR) nos dados da demanda turística do estado de Mato Grosso do Sul (MS). A amostra foi coletada na ferramenta base de dados extrator do Instituto Brasileiro de Turismo (Embratur) referente as chegadas turísticas por todas as vias registradas no MS entre janeiro de 2007 a dezembro de 2017. As previsões dos modelos de previsão ARIMA, Holt-Winters (HW) versões aditiva e multiplicativa e NNAR foram projetadas, por meio da linguagem de programação R, com uso do software R Studio. O procedimento empírico de execução dos scripts de todos os modelos foi disponibilizado. As predições fora da amostra da procura do turismo abrangeram o intervalo de janeiro até dezembro de 2018, sendo então comparadas aos dados reais do mesmo período. As previsões dos modelos foram comparadas no curto, médio e longo prazo mediante os critérios Mea Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD) e Mean Squared Error (MSD). A rede neural (NNAR) superou os modelos testados em diferentes horizontes de previsão e as medidas de erros mostraram que a NNAR é altamente precisa. Em segundo lugar no ranking de acuracidade destacou-se ARIMA. Os resultados mostraram que as previsões da rede neural auxiliam na tomada de decisão dos planejadores turísticos de MS. Para pesquisas futuras recomenda-se realizar previsões fora da amostra num amplo número de séries temporais.

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