Informatics Publishing Limited

Ataulla ¹, B. Ismail ², Mohammed Yunus ³

Affiliations
1 Department of Mathematics, HKBK College of Engineering, Bangalore–45.
2 Department of Statistics, Mangalore University, Mangalore-574199
3 HKBK College of Engineering, Bangalore–45

Abstract

The time frequency signal analysis, speech processing and other signal processing application is used in spectral estimation technique. Some shortcomings of recursive least square procedure necessitates in elevation computational power and the output achieved is numerically unsteadiness. So, the spectral proficiency of the signal is affected and a power error occurs in the estimator. In this paper an enhanced least square algorithm is proposed for enlightening the power signal spectral estimation. The proposed least square algorithm is the permutation of neural network. The persistence of the neural network, the power error of the linear and nonlinear signal is unwavering. So, the involvedness and the computational time of spectral estimation are abridged. To evaluate the estimation performance of proposed least square algorithm, the capon and amplitude and phase estimation based spectral analysis methods are used. The spectral estimation capability of the recommended least square algorithm is unwavering by the error weight of the signal. The proposed least square algorithm is implemented in MATLAB and the output performances are estimated.

Keywords

Signal Processing, Spectral Estimation, least Square Algorithm, Neural Network

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Godfrey Joseph Saqware ¹, B. Ismail ²

Affiliations
1 Department of Statistics, University of Dar Es Salaam, TZ
2 Department of Statistics, Yenepoya University, IN

Abstract

The stock markets are among the most volatile market worldwide. The future of these markets is daily affected by political instability and different enacted economic and government policies. Thus, the prediction and forecast of these markets are very important. The Bombay Stock Exchange (BSE) is the oldest stock market in Asia and India. This paper applied deep learning methods to predict the five companies closing prices under BSE. The selected companies based on market capitalization were Reliance Industries Ltd (RELI), TATA Consultancy Services (TCS), HDFC Bank Ltd (HDBK), Infosys Ltd (INFY), and ICICI Bank Ltd (ICBK). Based on Root Mean Square Error (RMSE), the traditional Bidirectional Long Short-Term Model (Bi-LSTM) model predicted well the HDBK closing prices. The Convolution Neural Networks (CNN) outperformed other models in predicting the ICBK, RELI, and INFY. The proposed Hybrid CNN-LSTM model with Bayesian hyperparameter tuning outperformed the CNN and Bi-LSTM models in predicting the TCS close price. Moreover, the hybrid model ranked second in predicting closing prices in all the selected companies. The next 100 days forecast shows high price volatility in the selected companies. In the closing prices forecasts, the hybrid CNN-LSTM model with Bayesian hyperparameter tuning has captured well the trend of the historical data. Additionally, Traders and financial analysts may easily understand the future market trend using the methods. Therefore, the powerful computer and more complex hybrid model may be applied to bring the best performance in terms of accuracy.

Keywords

Bayesian hyperparameter tuning, Bi-LSTM, Bombay Stock Exchange, CNN, and CNN-LSTM

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