[PDF] Top 20 Forecasting accuracy: a comparative study between artificial neural network and autoregressive model for streamflow
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Forecasting accuracy: a comparative study between artificial neural network and autoregressive model for streamflow
... AR model was commonly used to forecast annual ...the study fulfilled the ARMA assumptions which were linear and ...quarterly-monthly streamflow, it was proposed utilizing SARIMA, Deseasonalized ARMA ... See full document
9
COMPARATIVE ANALYSIS OF THE PERFORMANCE OF ARTIFICIAL NEURAL NETWORKS (ANNs) AND AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) MODELS ON RAINFALL FORECASTING
... to model monthly, biannual, yearly and quarterly Malaysia rainfall using the Focused Time Delay Neural Network (FTDNN) to make one-step-ahead ...yearly model gives the most accurate forecasts ... See full document
6
Demand forecasting Using Artificial Neural Network Based on Different Learning Methods: Comparative Analysis
... to study the effectiveness of forecasting the demand signals in the supply chain with ANN method and identify the best training ...This study has developed a comparative forecasting ... See full document
13
Application of Artificial Neural Network And Multiple Linear Regression Model for Forecasting of Container Throughput In APM Terminals Apapa Port A Comparative Approach
... This study is concerned with forecasting of container throughput volume in APM Terminals Apapa Lagos, ...Two forecasting methods were compared, namely linear regression model and ... See full document
16
FORECAST PERFORMANCE OF UNIVARIATE TIME SERIES AND ARTIFICIAL NEURAL NETWORK MODELS
... better model for forecasting Nigeria monthly Precipitation time series data that exhibit seasonal, periodic variations and non-linearity is ...Seasonal Autoregressive Integrated Moving Average ... See full document
5
Forecasting solid waste generation in Juba Town, South Sudan using Artificial Neural Networks (ANNs) and Autoregressive Moving Averages (ARMA)
... this study on time series models, we analyzed and compared the Artificial Neural Networks (ANNs) and the Autoregressive Moving Averages (ARMA) in forecasting the weekly amounts of solid ... See full document
13
Comparative study of static and dynamic neural network models for nonlinear time series forecasting
... decades, neural network models have been focused upon by researchers due to their more real performance and on this basis different types of these models have been used in ...in forecasting the ... See full document
18
International Journal of Computer Science and Mobile Computing
... and neural networks etc. A neural network is a massively parallel distributed processor that has a natural propensity for storing experimental knowledge and making it available for ...use. ... See full document
6
Model of Cholera Forecasting Using Artificial Neural Network in Chabahar City, Iran
... as accuracy of predic- tion. In the current study, comparison measurement was based on proper decrease of error and accuracy of predic- tion in test villages ... See full document
8
Comparative Study of Static and Dynamic Artificial Neural Network Models in Forecasting of Tehran Stock Exchange
... to model the complex systems (Wanous et al, ...all between zero and one; and third, Defuzzification, that includes the inverse transformation of obtained fuzzy numbers to the numerical data in the ... See full document
17
Credit Card Fraud Detection and Prevention - A Survey
... a comparative study of six fraud detection methods based on credit card (Artificial Immune System, Hidden Markov Model, Neural Network, Genetic Algorithm, Decision Tree and ... See full document
7
BANKRUPTCY PREDICTION OF FIRMS USING THE DATA MINING METHOD
... a study titled “A Comparative Study of Bankruptcy Prediction using Altman, Logit and Artificial Neural Network Models”, Garkaz and Barzegar Khandoozi (2010) analyzed this issue ... See full document
12
A New Approach for Rainfall Prediction using Artificial Neural Network
... weather forecasting. In the current world climate change, the accuracy of rainfall forecasting model is very important ...rainfall forecasting, were used an Artificial ... See full document
12
Study on Pollution Forecasting using 2Phase Neural Network
... Artificial Neural Network for Pollution Forecasting : Forecasting it is intuitive that accuracy is very important ...pollution forecasting model are different ... See full document
7
Smart Water: Short-Term Forecasting Application in Water Utilities
... ANN model was chosen for forecasting. Nonlinear Autoregressive (NAR) and Nonlinear Autoregressive with Exogenous inputs (NARX) are a supervised machine learning Multi-Layer Perceptron (MLP) ... See full document
70
Comparison of Response Surface Methodology and Hybrid-Training Approach of Artificial Neural Network in Modeling the Properties of Concrete Containing Steel Fiber Extracted from Waste Tires
... logic, neural networks, genetic algorithm and expert ...of artificial intelligence techniques that imitates the principles of biological ...of artificial neural network (ANN) is another ... See full document
17
Demand Forecasting in Deregulated Electricity Markets
... a neural network approach to forecast next day electricity demand in the electricity markets of ...neural network. This work also proposes two types of demand forecasting models: ... See full document
6
Parameters Optimization Using Genetic Algorithms in Support Vector Regression for Sales Volume Forecasting
... volume forecasting is the key to the entire budgeting ...volume forecasting in some way. If the sales volume forecasting is sloppily done, then the rest of the budgeting process is largely a waste of ... See full document
7
Improved Prediction of Wind Speed using Machine Learning
... useful information is difficult. The dataset in the modern era has high dimensionality and huge volume, which required an automated tool for processing. This confinement needs automated tools to mine useful information ... See full document
7
Classification and Forecasting of Bollywood Movies by Commercial Success using Back Propagation Neural Network model
... The accuracy levels of these works are quite high revolving around 85% except Narendra et ...Pinpoint accuracy had a sharp decline to ...satisfactory accuracy level of ... See full document
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