[PDF] Top 20 Comparison of time series forecasting with artificial neural network and statistical approach
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Comparison of time series forecasting with artificial neural network and statistical approach
... is comparison of real data forecast using statistical methods and artifi cial neu- ral network ...of statistical analysis are fi xed part of the decision process in the economical resolution ... See full document
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Comparison of exponential time series alignment and time series alignment using artificial neural networks by example of prediction of future development of stock prices of a specific company
... to time series ...of time series (but can also be used for other time series components) that are not time-stable and are rapidly ...prognostic time series ... See full document
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Application of Artificial Neural Network And Multiple Linear Regression Model for Forecasting of Container Throughput In APM Terminals Apapa Port A Comparative Approach
... with forecasting of container throughput volume in APM Terminals Apapa Lagos, ...Two forecasting methods were compared, namely linear regression model and Artificial Neural Network ... See full document
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Error Reduction based Demand Forecasting: An Appraisal of Kerala Power System
... for comparison of two methods like time series and neural network models to estimate short- term load forecasting and the prediction accuracy has been ...A neural ... See full document
6
Study on Pollution Forecasting using 2Phase Neural Network
... from time series analysis is to forecast (medium/long term) or to now cast (short term: 1 or 3 hours) the systems ...monitoring network is described in Table 1 and included meteorological parameters ... See full document
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Forecasting the yield and direction of the Australian 10 year Commonwealth Treasury Bond using artificial neural networks
... This paper is concerned with the application of artificial neural networks (ANN) to the forecasting of the time series generated by the 10 Year Commonwealth Treasury Bond [r] ... See full document
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Time Series Forecasting using Evolutionary Neural Network
... evolutionary neural networks (trained using evolutionary algorithms) are used for TSF, so that better forecast accuracy can be ...For comparison results obtained from evolutionary algorithms are compared ... See full document
5
Forecasting of heavy metals concentration in groundwater resources of Asadabad plain using artificial neural network approach
... for forecasting heavy metals concentration in the selected monitoring ...less time for training of the network compared to Bayesian regularization ... See full document
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Forecasting solid waste generation in Juba Town, South Sudan using Artificial Neural Networks (ANNs) and Autoregressive Moving Averages (ARMA)
... 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 waste ... See full document
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FORECAST PERFORMANCE OF UNIVARIATE TIME SERIES AND ARTIFICIAL NEURAL NETWORK MODELS
... Time series analysis has been used to model and obtain forecast across several fields (Wei, ...2000). Time series modelling involves a careful collection and rigorous study of past observation ... See full document
5
Comparison of three updating schemes using artificial neural network in flow forecasting
... In most cases, the ANN models used in hydrology are regarded as black-box models that cannot provide any physically realistic structure and parameters to represent the hydrological processes in catchments, even though ... See full document
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An overview of Artificial Intelligence techniques for efficient load forecasting
... load forecasting integrate infrastructure development, energy purchasing, and generation, contract evaluation as well as load switching ...the time of the day. This, in turn, assists to attain a systematic ... See full document
9
Modelling the Stock Price Volatility Using Asymmetry Garch and Ann-Asymmetry Garch Models
... financial time series analysis and ...is Artificial Neural Networks (ANN) ...standard statistical models have been used in the field of financial time series analysis and ... See full document
7
Artificial Neural Network Approach for Load Forecasting in Demand Side Management
... methods. Time of use tariffs and flat rates techniques are used less as they may not necessarily cut down the peak of energy usage but would adversely create a steep rise in off-peak ...of time base, ...of ... See full document
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Realized Volatility Forecasting with Neural Networks
... tificial neural networks as forecasting ...this approach is the possibility to approximate any linear and nonlinear behaviors without knowing the structure of the data generating ...for ... See full document
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Time Series Modeling of River Flow Using Wavelet Neural Networks
... nonlinear artificial neurons running in parallel, which can be generated, as one or multiple ...of artificial neu- rons was first introduced by McCulloch and Pitts [36], the major applications of ANN’s have ... See full document
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A hybrid approach on tourism demand forecasting
... recognition, forecasting, prediction and ...using artificial neural ...improving time series forecasting through the analysis of additional information, reducing its size and ... See full document
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Causal Method and Time Series Forecasting model based on Artificial Neural Network
... times series models are preferred for they have been used in many applications such as: Economic Forecasting, Sales Forecasting, Budgetary Analysis, Stock Market Analysis, Process and Quality Control ... See full document
6
Short-Term Forecast of Wind Speed through Mathematical Models
... models for forecasting time series applied in wind generation based on the combination of time series 828. models with artificial neural networks[r] ... See full document
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FCM BPSO: ENERGY EFFICIENT TASK BASED LOAD BALANCING IN CLOUD COMPUTING
... a time series data into linear and nonlinear form for further ...seasonal time series, firstly the seasonal component is removed by a linear model, such as a seasonal autoregressive model and ... See full document
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