scholarly journals Konstruksi Forecasting System Multi-Model untuk pemodelan matematika pada peramalan Indeks Pembangunan Manusia Provinsi Nusa Tenggara Barat

2018 ◽  
Vol 4 (2) ◽  
pp. 114 ◽  
Author(s):  
Lalu Sucipto ◽  
Syaharuddin Syaharuddin
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Graphical User Interface ◽  
Interpolation Method ◽  
Moving Average ◽  
Back Propagation ◽  
Naive Method ◽  
Forecasting System ◽  
Exponential Smoothing Method

Penelitian ini bertujuan untuk mengembangkan produk Forecasting System Multi-Model (FSM) guna menentukan metode terbaik dalam sistem peramalan (forecast) dengan mengkonstruksi beberapa metode dalam bentuk Graphical User Interface (GUI) Matlab dengan menghitung semua indikator tingkat akurasi guna menemukan model matematika terbaik dari data time series pada periode tertentu. Pada tahap simulasi, tim peneliti menggunakan data Indeks Pembangunan Manusia (IPM) Provinsi Nusa Tenggara Barat (NTB) tahun 2010-2017 guna memprediksi IPM NTB tahun 2018. Adapun metode yang diuji adalah Moving Average (SMA, WMA dan EMA), Exponential Smoothing Method (SES, Brown, Holt, dan Winter), Naive Method, Interpolation Method (Newton Gregory), dan Artificial Neural Network (Back Propagation). Kemudian model dievaluasi untuk melihat tingkat akurasi masing-masing metode berdasarkan nilai MAD, MSE, dan MAPE. Berdasarkan hasil simulasi data dari 10 metode yang diuji diketahui bahwa metode Holt paling akurat dengan hasil prediksi tahun 2018 sebesar 67,45  dengan MAD, MSE, dan MAPE berturut-turut sebesar 0,22654; 0,075955 dan 0,34829.   The purpose of this research is to develop a product was called Forecasting System Multi-Model (FSM) to determine the best method in the forecasting system by constructing several methods in the form of Graphical User Interface (GUI) Matlab. It was done by all indicator accuration to find the best mathematical model of time series data in a certain period. In the simulation phase, this research used the Human Development Index (HDI) data of West Nusa Tenggara (NTB) Province in 2010 - 2017 to predict the HDI data of NTB in 2018. The methods tested were Moving Average (SMA, WMA and EMA), Exponential Smoothing Method (SES, Brown, Holt, and Winter), Naive Method, Interpolation Method (Newton Gregory), and Artificial Neural Network (Back Propagation). Then the models/methods were evaluated to see the level of accuracy of each method based on the value of MAD, MSE, and MAPE. Based on data simulation result from 10 tested method known that Holt method is most accurate with prediction result of 2018 equal to 67,45 with MAD, MSE, and MAPE respectively equal to 0.22654, 0.075955 and 0.34829.

scholarly journals Rainfall Forecasting Using Various Artificial Neural Network Techniques - A Review

2021 ◽  
pp. 506-526
Author(s):  
Nisha Thakur ◽  
Sanjeev Karmakar ◽  
Sunita Soni
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Moving Average ◽  
Weather Prediction ◽  
Optimization Technique ◽  
Back Propagation ◽  
Rainfall Forecasting ◽  
Rainfall Prediction ◽  
Artificial Neural ◽  
Review Reports

The present review reports the work done by the various authors towards rainfall forecasting using the different techniques within Artificial Neural Network concepts. Back-Propagation, Auto-Regressive Moving Average (ARIMA), ANN , K- Nearest Neighbourhood (K-NN), Hybrid model (Wavelet-ANN), Hybrid Wavelet-NARX model, Rainfall-runoff models, (Two-stage optimization technique), Adaptive Basis Function Neural Network (ABFNN), Multilayer perceptron, etc., algorithms/technologies were reviewed. A tabular representation was used to compare the above-mentioned technologies for rainfall predictions. In most of the articles, training and testing, accuracy was found more than 95%. The rainfall prediction done using the ANN techniques was found much superior to the other techniques like Numerical Weather Prediction (NWP) and Statistical Method because of the non-linear and complex physical conditions affecting the occurrence of rainfall.

scholarly journals Estimation of Contact Tip to Work Distance (CTWD) using Artificial Neural Network (ANN) in GMAW

2019 ◽  
Vol 269 ◽  
pp. 04004
Author(s):  
Fuad Mahfudianto ◽  
Eakkachai Warinsiriruk ◽  
Sutep Joy-A-Ka
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Moving Average ◽  
Back Propagation ◽  
Welding Current ◽  
Travel Speed ◽  
Mean Square ◽  
Wire Electrode ◽  
Marquardt Algorithm ◽  
Artificial Neural

A method for optimizing monitoring by using Artificial Neural Network (ANN) technique was proposed based on instability of arc voltage signal and welding current signal of solid wire electrode (GMAW). This technique is not only for effective process modeling, but also to illustrate the correlation between the input and output parameters responses. The algorithms of monitoring were developed in time domain by carrying out the Moving Average (M.A) and Root Mean Square (RMS) based on the welding experiment parameters such as travel speed, thickness of specimen, feeding speed, and wire electrode diameter to detect and estimate with a satisfactory sample size. Experiment data was divided into three subsets: train (70%), validation (15%), and test (15%). Error back-propagation of Levenberg-Marquardt algorithm was used to train for this algorithm. The proposed algorithms on this paper were used to estimate the variety the Contact Tip to Work Distance (CTWD) through Mean Square Error (MSE). Based on the results, the algorithms have shown that be able to detect changes in CTWD automatically and real time with takes 0.147 seconds (MSE 0.0087).

scholarly journals Statistical modeling of monthly streamflow using time series and artificial neural network models: Hindiya Barrage as a case study

2021 ◽  
Author(s):  
Nabeel H. Al-Saati ◽  
Isam I. Omran ◽  
Alaa Ali Salman ◽  
Zainab Al-Saati ◽  
Khalid S. Hashim
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Mean Squared Error ◽  
Moving Average ◽  
Stationary Time Series ◽  
Coefficient Of Determination ◽  
Neural Network Models ◽  
Monthly Streamflow ◽  
Artificial Neural

Abstract Autoregressive Integrated Moving Average (ARIMA) Box-Jenkins models combine the autoregressive and moving average models to a stationary time series after the appropriate transformation, while the nonlinear autoregressive (N.A.R.) or the autoregressive neural network (ARNN) models are of the kind of multi-layer perceptron (M.L.P.), which compose an input layer, hidden layer and an output layer. Monthly streamflow at the downstream of the Euphrates River (Hindiya Barrage) /Iraq for the period January 2000 to December 2019 was modeled utilizing ARIMA and N.A.R. time series models. The predicted Box-Jenkins model was ARIMA (1,1,0) (0,1,1), while the predicted artificial neural network (N.A.R.) model was (M.L.P. 1-3-1). The results of the study indicate that the traditional Box-Jenkins model was more accurate than the N.A.R. model in modeling the monthly streamflow of the studied case. Performing a one-step-ahead forecast during the year 2019, the forecast accuracy between the forecasted and recorded monthly streamflow for both models was as follows: the Box-Jenkins model gave root mean squared error (RMSE = 48.7) and the coefficient of determination R2 = 0.801), while the (NAR) model gave (RMSE = 93.4) and R2 = 0.269). Future projection of the monthly stream flow through the year 2025, utilizing the Box-Jenkins model, indicated the existence of long-term periodicity.

scholarly journals Multifraktalitas dan Studi Komparatif Prediksi Indeks dengan Metode Arima dan Artificial Neural Network (ANN)

The Winners ◽  
2008 ◽  
Vol 9 (2) ◽  
pp. 112
Author(s):  
Harjum Muharam ◽  
Muhammad Panji
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Time Series Data ◽  
Moving Average ◽  
Back Propagation ◽  
Series Data ◽  
Future Value ◽  
Auto Regressive ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This paper discusses technical analysis widely used by investors. There are many methods that exist and used by investor to predict the future value of a stock. In this paper we start from finding the value of Hurst (H) exponent of LQ 45 Index to know the form of the Index. From H value, we could determinate that the time series data is purely random, or ergodic and ant persistent, or persistent to a certain trend. Two prediction tools were chosen, ARIMA (Auto Regressive Integrated Moving Average) which is the de facto standard for univariate prediction model in econometrics and Artificial Neural Network (ANN) Back Propagation. Data left from ARIMA is used as an input for both methods. We compared prediction error from each method to determine which method is better. The result shows that LQ45 Index is persistent to a certain trend therefore predictable and for outputted sample data ARIMA outperforms ANN.

scholarly journals Improving Daily Peak Flow Forecasts Using Hybrid Fourier-Series Autoregressive Integrated Moving Average and Recurrent Artificial Neural Network Models

AI ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. 263-275 ◽  
Author(s):  
Mohammad Ebrahim Banihabib ◽  
Reihaneh Bandari ◽  
Mohammad Valipour
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Hybrid Model ◽  
Peak Flow ◽  
Moving Average ◽  
Flow Time ◽  
Autoregressive Integrated Moving Average ◽  
Auto Regressive ◽  
Reservoir Inflow

In multi-purpose reservoirs, to achieve optimal operation, sophisticated models are required to forecast reservoir inflow in both short- and long-horizon times with an acceptable accuracy, particularly for peak flows. In this study, an auto-regressive hybrid model is proposed for long-horizon forecasting of daily reservoir inflow. The model is examined for a one-year horizon forecasting of high-oscillated daily flow time series. First, a Fourier-Series Filtered Autoregressive Integrated Moving Average (FSF-ARIMA) model is applied to forecast linear behavior of daily flow time series. Second, a Recurrent Artificial Neural Network (RANN) model is utilized to forecast FSF-ARIMA model’s residuals. The hybrid model follows the detail of observed flow time variation and forecasted peak flow more accurately than previous models. The proposed model enhances the ability to forecast reservoir inflow, especially in peak flows, compared to previous linear and nonlinear auto-regressive models. The hybrid model has a potential to decrease maximum and average forecasting error by 81% and 80%, respectively. The results of this investigation are useful for stakeholders and water resources managers to schedule optimum operation of multi-purpose reservoirs in controlling floods and generating hydropower.

scholarly journals PERAMALAN KUNJUNGAN WISATAWAN MANCANEGARA KE PROVINSI BALI MENGGUNAKAN METODE ARTIFICIAL NEURAL NETWORK

2020 ◽  
Vol 9 (4) ◽  
pp. 213
Author(s):  
I KETUT RESTU WIRANATA ◽  
G.K. GANDHIADI ◽  
LUH PUTU IDA HARINI
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Activation Function ◽  
Network Algorithms ◽  
Tourism Sector ◽  
Forecasting System ◽  
Artificial Neural ◽  
Tourism Potential ◽  
Sigmoid Activation Function

Bali has an increasing tourism potential. This is evidenced by the increasing number of foreign tourist visits to Bali Province each year. Although Bali's tourism trends have continued to increase over the past few years, efforts to improve the quality of Bali tourism need to be made. One way is to do forecasting. To support improvement efforts in Bali's tourism sector, the author created a forecasting system for foreign tourists to Bali province using artificial neural network methods with back propagation algorithms. Artificial Neural Networks with back propagation algorithms are neural network algorithms by finding optimal weight values. The forecast results using the binary sigmoid activation function were obtained by 489,862 foreign tourists in November 2019 with MAPE at 1.62% and 487,342 foreign tourists in December 2019 with MAPE of 11.78%. The forecast results using the bipolar sigmoid activation function were obtained by 493,200 foreign tourists in November 2019 with MAPE of 0.95% and 484,090 foreign tourists in December 2019 with MAPE of 12.37%.

Comparison of an Artificial Neural Network and a Multiple Linear Regression in Predicting the Heat of Combustion of Diesel Fuel Based on Hydrocarbon Groups

2020 ◽  
Vol 71 (6) ◽  
pp. 66-74
Author(s):  
Younis M. Younis ◽  
Salman H. Abbas ◽  
Farqad T. Najim ◽  
Firas Hashim Kamar ◽  
Gheorghe Nechifor
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Diesel Fuel ◽  
Back Propagation ◽  
Second Step ◽  
Heating Value ◽  
Network Training ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Propagation Network

A comparison between artificial neural network (ANN) and multiple linear regression (MLR) models was employed to predict the heat of combustion, and the gross and net heat values, of a diesel fuel engine, based on the chemical composition of the diesel fuel. One hundred and fifty samples of Iraqi diesel provided data from chromatographic analysis. Eight parameters were applied as inputs in order to predict the gross and net heat combustion of the diesel fuel. A trial-and-error method was used to determine the shape of the individual ANN. The results showed that the prediction accuracy of the ANN model was greater than that of the MLR model in predicting the gross heat value. The best neural network for predicting the gross heating value was a back-propagation network (8-8-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.98502 for the test data. In the same way, the best neural network for predicting the net heating value was a back-propagation network (8-5-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.95112 for the test data.

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