The present study is an attempt in applying the combination of dynamic neural network and decomposition of wavelet in order to make possible the selection of an optimized pattern for predicting considered variable. For the purpose of research, monthly time series of exc More
The present study is an attempt in applying the combination of dynamic neural network and decomposition of wavelet in order to make possible the selection of an optimized pattern for predicting considered variable. For the purpose of research, monthly time series of exchange rate from April 1998 to December 2012 were used including 177 observations from which 150 observations were used for modeling purpose and 27 observations were used for simulation or in other words for presenting predictions out of samples. The findings of present study imply that firstly, dynamic neural network models compared to feed-forward multilayer neural networks have better performance in predicting exchange rate out of sample, based on both criteria for prediction error calculation: MSE & RMSE and secondly, applying wavelet decomposition technique improves prediction results of mentioned models based on both criteria. The third point is that among mentioned models, the best result belongs to predictions obtained from dynamic neural networks based on decomposed data by wavelet technique. Therefore, applying this combination of models as an optimized combination is suggested to monetary researchers, analysts and decision makers of country.
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Because of very large frequency and volatility in Financial markets Indicators, acertain type of non stationary is created that it refers to the fraction non stationary. Thiscauses, provides Long memory in this type of time series. Hence, this study has inaddition to ex More
Because of very large frequency and volatility in Financial markets Indicators, acertain type of non stationary is created that it refers to the fraction non stationary. Thiscauses, provides Long memory in this type of time series. Hence, this study has inaddition to examine the existence of the long memory in both mean and varianceequations in the return series of Tehran stock exchange, Pays to forecasting the volatilityof this index. For this purpose, the daily data from fifth Farvardin 1388 to eighteenthOrdibehesht 1391 is used. Our results confirm the existence of Long Memory in bothmean and variance equations. However, among others, based on the information criteriaand MSE, ARFIMA (1,2)-FIGARCH(BBM) model has been selected as the bestspecification to model and forecast the volatility of Tehran stock exchange’s return. Aswell, in order to Forecasting the volatility of this series, was used Combination of theabove model with Level and decomposed data. The results show that, according to theforecasting error criteria (MSE and RMSE), the result of model’s based on decomposeddata (with wavelet technique), more acceptable.
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One method for determining motor commands to control hand prostheses is to use surface electr­omy­ogr­am (sEMG) signal patterns. Due to the random and non-stationary nature of the signal, the idea of using signal information in small time intervals was inves More
One method for determining motor commands to control hand prostheses is to use surface electr­omy­ogr­am (sEMG) signal patterns. Due to the random and non-stationary nature of the signal, the idea of using signal information in small time intervals was investigated. In this study, with the aim of more accurate and faster detection of hand movements, two signal decomposition methods, namely discrete wavelet transform (DWT) and empirical mode decomposition (EMD) were evaluated. The sEMG sign­als of the Ninapro-DB1 dataset, which were extracted from 27 healthy subjects while performing hand and finger movements, were used to design the system. Simple time domain features with fast calculation capability were extracted for each subband of the decomposed signals. Also, support vector machine (SVM) using different kernel functions was applied as a classifier. The results show that the use of DWT and EMD methods with the ability to access the information of time and frequency sub-intervals of the signals, provides better results in identifying hand movements compared to previous studies. With the EMD method and eight intrinsic mode functions (IMF), the highest recognition accuracy of 83.3% was obtained for six movements. Also, the DWT with the Bior5.5 mother wavelet and five levels of decomposition, achieved 80% recognition accuracy for ten movements and with the Coif2 mother wavelet and six levels of decomposition, the accuracy was 83.33% for eight movements. The results show the better performance of the DWT decomposition method compared to EMD for the design of the hand movement recognition system using sEMG signal patterns.
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In the current research,wavelet analysis is used to analyze the time series of prices in a pair of assets into general and detailed time series, and the property of collocation between different and corresponding levels of analysis of two series is checked in order to f More
In the current research,wavelet analysis is used to analyze the time series of prices in a pair of assets into general and detailed time series, and the property of collocation between different and corresponding levels of analysis of two series is checked in order to find collinear pairs at different levels of analysis. And then its profitability is examined. In this research, the profitability of the pair trading system based on wavelet analysis was investigated on 14 indices of the Tehran Stock Exchange betwee 2013-2022. The results show that for the second level of detail in the wavelet analysis, the results are quite impressive and the number of trading positions is more than doubled, the daily return is increased to four times and the Sharpe ratio is also increased to about two times. The system formed based on the first level of detail also has a better profitable performance than the normal aggregation, and the performance of the third level of detail is within the limits of aggregation. In addition, the average duration of the transaction also shows significant decrease in the first and second levels. Profitability performance at the level of general series is generally weaker than the aggregate.
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