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Soft computing forecasting tools play an important role to forecast many complicated systems. In this paper, an effort has been made to use soft computing approaches to predict Dhaka daily temperatures for the period of 28 February 1945 to 27 August 2006. We have selected the fuzzy neuro model, the neuro genetic algorithm model as soft computing techniques. To compare results, a popular time series...
Neural Network is a network that resembles a human brain tissue, which may infer a result based on the facts or experience that happened. Many applications have implemented neural network. In this thesis, we compared the stock forecasting result of ANTM (PT Aneka Tam bang) using Artificial Neural Network and ARIMA. ARIMA is a technique of time-series forecasting, which means forecast based on the...
Prediction models based on different concepts have been proposed in recent years. The accuracy rates resulting from linear models such as exponential smoothing, linear regression (LR) and autoregressive integrated moving average (ARIMA) are not high as they are poor in handling the nonlinear relationships among the data. Neural network models are considered to be better in handling such nonlinear...
Electricity price forecasting is becoming increasingly relevant to power producers and consumers in the new competitive electric power markets, when planning bidding strategies in order to maximize their benefits and utilities, respectively. This paper proposed a method to predict hourly electricity prices for next-day electricity markets by combination methodology of ARIMA and ANN models. The proposed...
The time series of monthly cigarette sales have double trends which include long-term upward trend and seasonal fluctuations trend. For this complex system forecasting, single linear or nonlinear forecasting model can't deeply capture characteristics of the data so the results are imprecise. In this paper, a combined methodology that combines both ARIMA and GMDH models is proposed to take advantage...
In the framework of competitive electricity markets, power producers and consumers need accurate price forecasting tools. Price forecasts embody crucial information for producers and consumers when planning bidding strategies in order to maximize their benefits and utilities, respectively. The choice of the forecasting model becomes the important influence factor how to improve price forecasting accuracy...
Precision is important in judging measure instruments quality and tracing to the source of measure errors. recision forecast present an effective precision control methods, but forecast and combined forecast technology is researched less in measuring instruments precision forecast. The theory of Linear combination forecast is very simple and it be applied in many projects, but it has some drawbacks,...
Since ANNs model could capture the nonlinearity of time series, it performances well on forecasting time series. Hybrid or combined ANNs with ARMA models are extensively studied and used in financial time series forecasting. But we doubt the necessity to build the hybrid models to forecast time series. Do hybrid models always outperform the single ANNs models? This paper is aimed to answer it. Two...
Anomaly detection of self-similar network traffic data is a difficult problem in network management. Due to network traffic may have the property of long term dependent, it can be approximated with a finite order Euler-Cauchy system based on ARMA (autoregressive moving average). As a result, AR (auto-regressive) model based on time serial analysis theory was used to deal with the problem of self-similar...
Stock market forecasting has attracted a lot of research interests in previous literature. Traditionally, the autoregressive moving average (ARMA) model has been one of the most widely used linear models in time series forecasting. However, the ARMA model cannot easily capture the nonlinear patterns. And recent studies have shown that artificial neural networks (ANN) method achieved better performance...
According to the complexity of the traffic historical data and the randomness of a lot of uncertain factors influence, a hybrid predicting model that combines both autoregressive integrated moving average (ARIMA) and multilayer artificial neural network (MLANN) is proposed in this paper. ARIMA is suitable for linear prediction and MLFNN is suitable for nonlinear prediction. This paper also investigates...
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