scholarly journals Pemilihan Arsitektur Terbaik pada Model Deep Learning Melalui Pendekatan Desain Eksperimen untuk Peramalan Deret Waktu Nonlinier

2019 ◽  
Vol 18 (2) ◽  
pp. 153-159
Author(s):  
Novri Suhermi ◽  
Suhartono Suhartono ◽  
I Made Gde Meranggi Dana ◽  
Dedy Dwi Prastyo
Keyword(s):  
Deep Learning ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Exponential Smoothing ◽  
Mean Square ◽  
Feedforward Network ◽  
Auto Regressive ◽  
Hidden Layer

Penentuan arsitektur model deep learning yang tepat merupakan hal yang sangat esensial untukmendapatkan hasil ramalan dengan tingkat kesalahan minimum. Arsitektur deep learning meliputijumlah input dan variabel apa saja yang digunakan, jumlah hidden layer, jumlah neuron pada setiaphidden layer, dan fungsi aktivasi. Pada penelitian ini dilakukan studi simulasi pada salah satu modeldeep learning, yaitu deep feedforward network, dengan berbagai kombinasi arsitektur untukmendapatkan arsitektur paling optimum. Data yang digunakan merupakan data bangkitan yangmengikuti model nonlinier Exponential Smoothing Transition Auto-regressive (ESTAR) sebanyak 1000data, di mana 900 data digunakan sebagai data training dan 100 data digunakan sebagai datatesting. Ukuran evaluasi model yang digunakan adalah root mean square error of prediction (RMSEP).Hasil empiris yang didapatkan di antaranya, pemilihan input yang tepat dapat meningkatkanakurasi peramalan, serta pemilihan fungsi aktivasi dan kedalaman arsitektur sangat diperlukanuntuk mendapatkan hasil ramalan yang semakin optimum.

scholarly journals Forecasting hourly emergency department arrival using time series analysis

2020 ◽  
Vol 26 (1) ◽  
pp. 34-43
Author(s):  
Avishek Choudhury ◽  
Estefania Urena
Keyword(s):  
Emergency Department ◽  
Time Series ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Moving Average ◽  
Time Series Forecasting ◽  
Mean Square ◽  
Mean Error ◽  
Auto Regressive

Background/aims The stochastic arrival of patients at hospital emergency departments complicates their management. More than 50% of a hospital's emergency department tends to operate beyond its normal capacity and eventually fails to deliver high-quality care. To address this concern, much research has been carried out using yearly, monthly and weekly time-series forecasting. This article discusses the use of hourly time-series forecasting to help improve emergency department management by predicting the arrival of future patients. Methods Emergency department admission data from January 2014 to August 2017 was retrieved from a hospital in Iowa. The auto-regressive integrated moving average (ARIMA), Holt–Winters, TBATS, and neural network methods were implemented and compared as forecasters of hourly patient arrivals. Results The auto-regressive integrated moving average (3,0,0) (2,1,0) was selected as the best fit model, with minimum Akaike information criterion and Schwartz Bayesian criterion. The model was stationary and qualified under the Box–Ljung correlation test and the Jarque–Bera test for normality. The mean error and root mean square error were selected as performance measures. A mean error of 1.001 and a root mean square error of 1.55 were obtained. Conclusions The auto-regressive integrated moving average can be used to provide hourly forecasts for emergency department arrivals and can be implemented as a decision support system to aid staff when scheduling and adjusting emergency department arrivals.

Deep Learning Stream Temperature Model: Recommendations for Modeling Gauged and Ungauged Basins

2021 ◽  
Author(s):  
Farshid Rahmani ◽  
Kathryn Lawson ◽  
Samantha Oliver ◽  
Alison Appling ◽  
Chaopeng Shen
Keyword(s):  
Deep Learning ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
The United States ◽  
Data Availability ◽  
Efficiency Coefficient ◽  
Mean Square ◽  
Training Set ◽  
The Impact

<p>Stream water temperature (T<sub>s</sub>) is a variable that plays a pivotal role in managing water resources. We used the long short-term memory (LSTM) deep learning architecture to develop a basin centric single T<sub>s</sub> model based on general meteorological data and basin meteo-geological attributes. We created a strong tool for long-term Ts projection and subsequently, improved the Ts model using novel approaches. We investigated the impact of both observed and simulated streamflow data on improving the model accuracy. At a national scale, we obtained a median root-mean-square error (RMSE) of 0.69 <sup>o</sup>C, and Nash-Sutcliffe model efficiency coefficient (NSE) of 0.985, which are marked improvements over previous values reported in previous studies. In order to test the performance of the model on basins ranging from basins with extensive data to unmonitored basins, we used more than 400 basins with different data-availability groups (DAG) across the continent of the United States to explore how to assemble the training dataset for both monitored and unmonitored basins. Best root-mean-square error (RMSE) for sites with extensive (99%), intermediate (60%), scarce (10%) and absent (0%) data for training were 0.75, 0.837, 0.889, and 1.595 <sup>o</sup>C, respectively. We observed the negative effect of the presence of reservoirs in T<sub>s</sub> modeling. Our results illustrated that the most suitable training set should be different in modeling basins with different availability of observed data. for predicting T<sub>s</sub> in a monitored basin, including basins that have at least equal DAG with that particular basin will result in most accurate predictions, however, for T<sub>s</sub> prediction in ungauged basin, including all basins in training section will generate the best model, showing a more diverse training set. Furthermore, to decrease overfitting produced by attributes for PUB application, we could improve the accuracy of the model using input-selection ensemble method. We got median correlation higher than 0.90 for PUB after seasonality was removed which is still high. While many T<sub>s</sub> prediction models showed better performance in summer, our model was on the opposite side. We found a strong relationship between general available daily meteorological variables and catchment attributes with the presented T<sub>s</sub> model. However, our results indicate that combining physics-based criteria to the model can improve the prediction of temperature in river networks.</p><p>.</p>

scholarly journals ANALISIS MODEL PROYEKSI LADA SEBAGAI KAJIAN DIVERSIFIKASI SEKTOR PENDAPATAN MASYARAKAT DI PROVINSI KEPULAUAN BANGKA BELITUNG

2021 ◽  
Vol 15 (2) ◽  
pp. 249-256
Author(s):  
Sisilia Jesika Pririzki ◽  
Ilam Maryam ◽  
Pitra Wati ◽  
Desy Yuliana Dalimunthe
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Exponential Smoothing ◽  
Mean Square ◽  
Fitting Model

Provinsi Kepulauan Bangka Belitung merupakan provinsi dengan pendapatan masyarakatnya bergantung pada sektor pertanian, yaitu lada. Penelitian ini bertujuan untuk memperoleh nilai proyeksi produksi lada di Provinsi Kepulauan Bangka Belitung pada Tahun 2022 yang selama ini menjadi komoditas utama dari sisi sektor pendapatan masyarakat dan juga merupakan bagian dari proses diversifikasi agar masyarakat setempat tidak bergantung dari satu sektor pertanian saja dalam memenuhi kebutuhan sehari-hari. Metode exponential smoothing yang digunakan dalam penelitian ini terdiri dari beberapa model, yakni simple, holt, dan brown exponential smoothing. Dari ketiga model ini akan ditentukan model peramalan yang terbaik (fitting model) dengan menggunakan hasil Root Mean Square Error (RMSE) yang terkecil dari ketiga model tersebut. Berdasarkan proses fitting model yang dilakukan, model holt merupakan model terbaik dengan nilai MSE 7.425,298 dan juga memberikan hasil bahwa komoditas lada ini mengalami penurunan sebesar 17,56% pada tahun 2021 dan juga mengalami penurunan sebesar 21,30% pada tahun 2022.

scholarly journals Metode Peramalan moving Average, 1000 ANALISIS PERBANDINGAN PERAMALAN PERMINTAAN PELUMAS PT XYZ DENGAN METODE MOVING AVERAGE, EXPONENTIAL SMOOTHING DAN NAIVE METHOD

2021 ◽  
Vol 2020 (1) ◽  
pp. 1000-1010
Author(s):  
Destia Anisya Ramdani ◽  
Fahriza Nurul Azizah
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Mean Absolute Deviation ◽  
Mean Square ◽  
Absolute Deviation ◽  
Naive Method

Pelumas merupakan produk dari PT XYZ yang digunakan untuk kendaraan dan mesin-mesin industri. Peramalan umumnya dilakukan untuk meramalkan jumlah produksi di masa mendatang dengan menggunakan data historis atau data-data pada permintaan sebelumnya terhadap produk perusahaan. Penelitian ini dilakukan untuk menguji enam metode peramalan agar dapat mengetahui metode mana yang tepat untuk diterapkan pada PT XYZ. Peramalan pada PT XYZ ini menggunakan data historis permintaan tahun 2019 dari bulan januari hingga bulan desember yang telah merepresentasikan pola permintaan setiap tahun di PT XYZ. Data ini digunakan untuk meramalkan setahun kedepan.Penelitian kali ini akan membandingkan enam metode peramalan diantaranya metode moving average 3 bulanan, moving average 5 bulanan, exponential smoothing dengan α=0,1, exponential smoothing dengan α=0,5, exponential smoothing dengan α=0,9 dan naive method. Untuk bahan perbandingan dari keenam metode yang telah disebutkan maka diberikan peramalan yaitu dengan metode penyimpangan Mean Absolute Deviation (MAD), Mean Square Error (MSE), Root Mean Square Error (RMSE), dan Absolute Presentage Error (MAPE).Hasil penelitian ini menunjukkan metode peramalan exponential smoothing dengan α=0,9 dengan nilai penyimpangan MAD 2.364,50, MSE 12.448.875,06, RMSE 3.528,30 dan MAPE 0,60 dapat dikatakan metode yang lebih optimal untuk diterapkan di PT XYZ karena memiliki nilai penyimpangan paling rendah dari metode moving average 3 bulanan, moving average 5 bulanan, exponential smoothing dengan α=0,1, exponential smoothing dengan α=0,5 dan naive method.Sehingga PT XYZ untuk menentukan tingkat permintaan konsumen dapat menggunakan metode exponential smoothing dengan α=0,9, karena setelah dilakukan perbandingan dari hasil penyimpangan setiap metode dan telah terbukti bahwasannya metode exponential smoothing dengan α=0,9 memiliki nilai penyimpangan MAD 2.364,60, MSE 12.448.875,06, RMSE 3.528,30 dan MAPE 0,60 yang artinya merupakan nilai penyimpangan terkecil dari metode moving average 3 bulanan, moving average 5 bulanan, exponential smoothing dengan α=0,1, exponential smoothing dengan α=0,5, dan naive method.

scholarly journals Prediksi Harga Beras Menggunakan Metode Multilayer Perceptron (MLP) dan Long Short Term Memory (LSTM)

2020 ◽  
Vol 12 (1) ◽  
pp. 35-41
Author(s):  
Steven Sen ◽  
Dedy Sugiarto ◽  
Abdul Rochman
Keyword(s):  
Deep Learning ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Multilayer Perceptron ◽  
Short Term Memory ◽  
Mean Square ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Hidden Layer

Beras adalah salah satu komoditas utama di masyarakat Indonesia. Masalah utama dengan beras secara nasional adalah inflasi harga beras. Oleh karena itu, penelitian ini memprediksi harga beras menggunakan arsitektur jaringan saraf tiruan Multilayer Perceptron (MLP) dan deep learning : Long Short Term Memory (LSTM) untuk mengantisipasi masalah ini. Data yang digunakan dalam penelitian ini adalah data riil harga beras selama 2016 - 2019 yang diperoleh dari PT. Food Station. Total dataset adalah 1307 dengan distribusi 1123 sebagai data train dan 184 sebagai data uji. Hasil akhir yang diperoleh dalam penelitian ini adalah LSTM lebih unggul dari MLP, dengan nilai Root Mean Square Error (RMSE) data train : 0,49, dan nilai loss RMSE dari data tes adalah 0,27. Model LSTM paling optimal dari 3 tes dilakukan, yaitu jumlah hidden layer = 16 dan epochs = 150 kali.

PERAMALAN BEBAN JANGKA PENDEK SISTEM KELISTRIKAN KOTA BATU MENGGUNAKAN DEEP LEARNING LONG SHORT-TERM MEMORY

Transmisi ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 97-102
Author(s):  
Heru Purnomo ◽  
Hadi Suyono ◽  
Rini Nur Hasanah
Keyword(s):  
Deep Learning ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Short Term Memory ◽  
Mean Square ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Dalam rangka proyeksi kebutuhan listrik dimasa mendatang, maka penyedia listrik dapat melakukan peramalan terkait besarnya kebutuhan dan permintaan energi listrik. Apabila besarnya permintaan listrik tidak dilakukan peramalan, maka akan terjadi kelebihan kapasitas yang menyebabkan tidak terserapnya sumber energi yang tersedia. Berdasarkan hasil penelitian diperoleh kesimpulan bahwa model terbaik dari metode Deep Learning LSTM  yang digunakan untuk melakukan prakiraan beban konsumsi listrik jangka pendek memiliki nilai RMSE (Root Mean Square Error) yang kecil Artinya tingkat akurasi dari metode Deep Learning LSTM tersebut lebih baik daripada ARIMA, hasil tersebut menunjukkan bahwa metode Deep Learning LSTM layak digunakan untuk memprakirakan beban konsumsi listrik jangka pendek di Kota Batu.

scholarly journals Approach to COVID-19 time series data using deep learning and spectral analysis methods

2021 ◽  
Vol 9 (1) ◽  
pp. 1-21
Author(s):  
Kayode Oshinubi ◽  
◽  
Augustina Amakor ◽  
Olumuyiwa James Peter ◽  
Mustapha Rachdi ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Spectral Analysis ◽  
Deep Learning ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Time Series Data ◽  
Series Data ◽  
Mean Square

<abstract> <p>This article focuses on the application of deep learning and spectral analysis to epidemiology time series data, which has recently piqued the interest of some researchers. The COVID-19 virus is still mutating, particularly the delta and omicron variants, which are known for their high level of contagiousness, but policymakers and governments are resolute in combating the pandemic's spread through a recent massive vaccination campaign of their population. We used extreme machine learning (ELM), multilayer perceptron (MLP), long short-term neural network (LSTM), gated recurrent unit (GRU), convolution neural network (CNN) and deep neural network (DNN) methods on time series data from the start of the pandemic in France, Russia, Turkey, India, United states of America (USA), Brazil and United Kingdom (UK) until September 3, 2021 to predict the daily new cases and daily deaths at different waves of the pandemic in countries considered while using root mean square error (RMSE) and relative root mean square error (rRMSE) to measure the performance of these methods. We used the spectral analysis method to convert time (days) to frequency in order to analyze the peaks of frequency and periodicity of the time series data. We also forecasted the future pandemic evolution by using ELM, MLP, and spectral analysis. Moreover, MLP achieved best performance for both daily new cases and deaths based on the evaluation metrics used. Furthermore, we discovered that errors for daily deaths are much lower than those for daily new cases. While the performance of models varies, prediction and forecasting during the period of vaccination and recent cases confirm the pandemic's prevalence level in the countries under consideration. Finally, some of the peaks observed in the time series data correspond with the proven pattern of weekly peaks that is unique to the COVID-19 time series data.</p> </abstract>

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.

scholarly journals Prediction of hydrological time-series using extreme learning machine

2015 ◽  
Vol 18 (2) ◽  
pp. 345-353 ◽  
Author(s):  
Md Atiquzzaman ◽  
Jaya Kandasamy
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Extreme Learning Machine ◽  
Mean Square ◽  
Feed Forward Neural Networks ◽  
Learning Machine ◽  
Hidden Layer

Applying feed-forward neural networks has been limited due to the use of conventional gradient-based slow learning algorithms in training and iterative determination of network parameters. This paper demonstrates a method that partly overcomes these problems by using an extreme learning machine (ELM) which predicts the hydrological time-series very quickly. ELMs, also called single-hidden layer feed-forward neural networks (SLFNs), are able to well generalize the performance for extremely complex problems. ELM randomly chooses a single hidden layer and analytically determines the weights to predict the output. The ELM method was applied to predict hydrological flow series for the Tryggevælde Catchment, Denmark and for the Mississippi River at Vicksburg, USA. The results confirmed that ELM's performance was similar or better in terms of root mean square error (RMSE) and normalized root mean square error (NRMSE) compared to ANN and other previously published techniques, namely evolutionary computation based support vector machine (EC-SVM), standard chaotic approach and inverse approach.

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