[PDF] Top 20 A Neural Network Approach to Time Series Forecasting
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A Neural Network Approach to Time Series Forecasting
... the time series forecasting. GRNN is a neural network proposed by Donald ...heterogeneous forecasting models adequate to local ...needs to be specified, but our ... See full document
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Linear and Non-linear time series analysis: forecasting financial markets
... needs to be done in order to define the frontiers were ANNs and the traditional methods can be more effective with a greater accuracy in relation to each ...tasks, neural networks will never ... See full document
66
A feature fusion based forecasting model for financial time series.
... method to predict time series using ICA as a pre-processing ...multivariable time series ...ANNs to predict Customer Relationship Management, and the experimen- tal result shows ... See full document
13
A Comparison on Statistical Methods and Long Short Term Memory Network Forecasting the Demand of Fresh Fish Products
... added to improve the accuracy of the current ...need to refactor to include the new ...When forecasting the overall demand, only 2 variables were used modelling the LSTM, differencing and ... See full document
58
A Survey Paper on Crime Prediction Technique Using Data Mining
... or time. A Good theoretical understanding is needed to provide practical crime prevention solutions that equivalent to specific places and ...data to predict future crime locations and ... See full document
5
NEURAL NETWORK APPROACH FOR EYE DETECTION
... driver to prevent from collision caused by drowsy ...complex.Neural Network based algorithm is proposed in this paper to detect the eyes ...the neural Network is trained to ... See full document
13
An Artificial Neural Network for Data Forecasting Purposes
... of time series prediction. According to the efficient market theory proposed in [1], the stock price follows a random path and it is practically impossible to make a particular long-term ... See full document
12
Time Series Forecasting of Energy Commodity using Grey Wolf Optimizer
... various forecasting techniques for the said time series ...price forecasting was implemented based on an improved Back Propagation Neural Network ...favour to the improved ... See full document
6
Multivariate synthetic streamflow generation using a hybrid model based on artificial neural networks
... NN approach for nonlinear modelling of multivariate streamflow ...inflows to the two ...streamflow series using multi-layer feed- forward error-backpropagation NN, with iterated multi-step ... See full document
14
An artificial neural network model for rainfall forecasting in Bangkok, Thailand
... 1990 for systematic and efficient management of operation and control of flood pro- tection facilities. BMA has 53 online tipping bucket type rain gauge stations scattered throughout Bangkok and sensors installed at the ... See full document
36
Agent-based distributed time series forecasting system
... economic forecasting is based almost only on the theory of probability and mathematical ...artificial neural networks, pattern recognition, genetic algorithms, ...order to reproduce the economic ... See full document
11
EUROPEAN UNION’S HISTORY, CULTURE AND CITIZENSHIP
... a forecasting model in predicting tourism demand, as represented by the number of Monthly Guest Nights in Hotels in the North of ...Artificial Neural Networks (ANN) methodology is employed to ... See full document
10
A Neural Network Model for Forecasting CO2 Emission
... is to determine the concentrations of a given pollutant for a known set of input ...artiicial neural network model (ANN), you can perform this task much more ...easily. To understand the ... See full document
6
Short Term Electrical Load Forecasting by Artificial Neural Network
... generation to end users (customers), ideally, power systems have to be built to meet the maximum demand, the so called peak load, to insure that sufficient power can be delivered to the ... See full document
4
A 2D Hopfield Neural Network approach to mechanical beam damage detection
... Hopfield Neural Networks (HNN) has proved to be an efficient method for online parameter estimation in one-dimensional processes ...the time-evolving estimates of the relevant ...converge to ... See full document
15
Short-Term Load Forecasting Using Artificial Neural Network
... function, network architecture and other network and training parameters is a trial and error approach until the best set is ...trial network parameters and their results. The input data used ... See full document
6
Electricity Load Forecasting based on Framelet Neural Network Technique
... of neural networks and time series techniques and using Framelet decomposition technique to explore more details of the time series ...perception neural network is ... See full document
4
Forecasting of a Non-Seasonal Tourism Time Series with ANN
... tourism forecasting. The most commonly used AI methods are artificial neural network (ANN) models (Kon & Turner, 2005; Palmer et ...according to Song and Li (2008) there is no one model ... See full document
11
Short-term electricity prices forecasting in a competitive market: A neural network approach
... with neural networks involves two steps: train- ing and ...presented to the network. The adequate selection of inputs for neural net- work training is highly influential to the success ... See full document
8
A framework for neural network to make business forecasting with hybrid VAR and GA components
... with neural network can improve the NN’s prediction ...out to demonstrate how to build our VAR-NN-GA system and its ...recently, neural network is introduced into the tourist ... See full document
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