[PDF] Top 20 A Neuro-wavelet Method for the Forecasting of Financial Time Series
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A Neuro-wavelet Method for the Forecasting of Financial Time Series
... Haar wavelet is a good differencing filter and as such is appropriate for capturing fluctuations in adjacent observations similar to the changes occurring on the stock returns ...selected wavelet ... See full document
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Financial time series forecasting using Simulated Annealing and Support Vector Regression
... for forecasting time series; however, there are other methods that have shown better ...of financial time series from the BMV, ARIMA and GARCH are commonly used; however, ... See full document
9
Performance evaluation of series and parallel strategies for financial time series forecasting
... real-world time series, particularly financial time series, is a critical task that has recently received overwhelming ...accurate forecasting, several related methods have been ... See full document
24
Escalation of Forecasting Accuracy through Linear Combiners of Predictive Models
... in financial time series is a challenging and complex task as it is highly associated with uncertainties, nonlinearity, etc and talks to the current economical as well as political ...enhanced ... See full document
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Online Full Text
... two time series has been relative to other ones. In the forecasting phases, AR model is not the only one ...the financial time series. The accuracy among those forecasting ... See full document
6
Modelling of crude oil prices using hybrid arima-garch model
... using time series models. Eventhough ARIMA is a popular method for forecasting, it is not able to handle volatile ...for forecasting different types of time series to ... See full document
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Extreme learning with chemical reaction optimization for stock volatility prediction
... for financial time series prediction include multilayer perceptron (Wang et ...return forecasting is demonstrated by Zhong and Enke (Zhong & Enke, 2017; Zhong & Enke, ...of ... See full document
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Time Series Forecasting Using Wavelet Least Squares Support Vector Machines and Wavelet Regression Models for Monthly Stream Flow Data
... new method based on the WLSSVM is developed by combining the discrete wavelet transforms (DWT) and least square support vector machines (LSSVM) model for forecasting ...streamflow time ... See full document
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Causal Method and Time Series Forecasting model based on Artificial Neural Network
... Demand forecasting plays a crucial role in the supply chain of today’s ...all forecasting methods, neural networks models are capable of delivering the best results if they are properly ...demand: ... See full document
6
Time Series Forecasting with Missing Values
... Abstract—Time series prediction has become more popular in various kinds of applications such as weather prediction, control engineering, financial analysis, industrial monitoring, ...original ... See full document
6
Forecasting Outlier Occurrence in Stock Market Time Series Based on Wavelet Transform and Adaptive ELM Algorithm
... sion, Box-Jenkins autoregressive integrated moving average (ARIMA), generalized autoregressive conditionally heteroskedastic (GARCH) methods, and modern methods applying artificial intelligence techniques including ... See full document
7
The enhanced group method of data handling models for time series forecasting
... iterative method is ...the time series or the system is very complex, it does not guarantee to obtain a good prediction accuracy by using the conventional polynomial function type neural ... See full document
48
A hybrid group method of data handling (GMDH) with the Wavelet Decomposition for Time Series Forecasting: A review
... Hybridization of existing competitive modeling methodologies is now an active area of research. For example, Sakaguchi et al. (2003)(Sakaguchi et al., 2003) proposed the use of GMDH and two kinds of genetic algorithm ... See full document
9
CRYTICAL ANALYSIS OF EXPONENTIAL SMOOTHING METHODS FOR FORECASTING
... Financial forecasting is important for an organization; it enables management to change operations at the right time in order to reap the greatest ...targets. Forecasting is also important ... See full document
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WiMAX Traffic Forecasting on Daily basis with Trainable Cascade-Forward Backpropagation Network in Wavelet Domain
... for time series forecasting assume that time series is issued from linear processes, but it may be totally inappropriate if the underlying mechanism is nonlinear ...the time ... See full document
7
A Survey of Stock Forecasting Model Based on Artificial Intelligence Algorithm
... non-stationary time series into time series components at different scales by wavelet decomposition and ...prediction method, this method not only improves the prediction ... See full document
6
Time Series Modeling of River Flow Using Wavelet Neural Networks
... years, wavelet theory has been introduced in the field of hydrology ...[17,20-22]. Wavelet analysis has recently been identified as a useful tool for describing both rainfall and runoff ... See full document
10
Rainfall Prediction using Neural Net based Frequency Analysis Approach
... India’s agriculture production is highly dependent on its precipitation behavior of the monsoon rainfall. Monsoon is the main source of water. Average rainfall prediction is a prime important factor for crop planning. ... See full document
5
ForecastingRevPARinaDecliningMarket-AnApplicationofTime.pdf
... There are three basic versions of exponential smoothing methods which are commonly used: 1) simple exponential smoothing (Brown, 1959), 2) trend-corrected exponential smoothing (Holt, 1957), and 3) Holt-Winters’ ... See full document
5
Developing defuzzifying method of fuzzy time-variant series for forecasting product demand
... Fuzzy time series are classified into two categories, fuzzy time-invariant and fuzzy time-variant series(Song and Chissom, ...fuzzy time-invariant series is applied by two ... See full document
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