Browsing by Author "Naik, N."

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Application of Modeling and Control Approaches of Piezoelectric Actuators: A Review
(Multidisciplinary Digital Publishing Institute (MDPI), 2023) Kanchan, M.; Mohith, M.; Bhat, R.; Naik, N.
Piezoelectric actuators find extensive application in delivering precision motion in the micrometer to nanometer range. The advantages of a broader range of motion, rapid response, higher stiffness, and large actuation force from piezoelectric actuators make them suitable for precision positioning applications. However, the inherent nonlinearity in the piezoelectric actuators under dynamic working conditions severely affects the accuracy of the generated motion. The nonlinearity in the piezoelectric actuators arises from hysteresis, creep, and vibration, which affect the performance of the piezoelectric actuator. Thus, there is a need for appropriate modeling and control approaches for piezoelectric actuators, which can model the nonlinearity phenomenon and provide adequate compensation to achieve higher motion accuracy. The present review covers different methods adopted for overcoming the nonlinearity issues in piezoelectric actuators. This review highlights the charge-based and voltage-based control methods that drive the piezoelectric actuators. The survey also includes different modeling approaches for the creep and hysteresis phenomenon of the piezoelectric actuators. In addition, the present review also highlights different control strategies and their applications in various types of piezoelectric actuators. An attempt is also made to compare the piezoelectric actuator’s different modeling and control approaches and highlight prospects. © 2023 by the authors. Licensee MDPI, Basel, Switzerland.
Assessment of land use and land cover change detection and prediction using deep learning techniques for the southwestern coastal region, Goa, India
(Springer Science and Business Media Deutschland GmbH, 2024) Naik, N.; Chandrasekaran, K.; Sundaram, V.; Prabhavathy, P.
Understanding the connections between human activities and the natural environment depends heavily on information about land use and land cover (LULC) in the form of accurate LULC maps. Environmental monitoring using deep learning (DL) is rapidly growing to preserve a sustainable environment in the long term. For establishing effective policies, regulations, and implementation, DL can be a valuable tool for assessing environmental conditions and natural resources that will positively impact the ecosystem. This paper presents the assessment of land use and land cover change detection (LULCCD) and prediction using DL techniques for the southwestern coastal region, Goa, also known as the tourist destination of India. It consists of three components: (i) change detection (CD), (ii) quantification of LULC changes, and (iii) prediction. A new CD assessment framework, Spatio-Temporal Encoder-Decoder Self Attention Network (STEDSAN), is proposed for the LULCCD process. A dual branch encoder-decoder network is constructed using strided convolution with downsampling for the encoder and transpose convolution with upsampling for the decoder to assess the bitemporal images spatially. The self-attention (SA) mechanism captures the complex global spatial-temporal (ST) interactions between individual pixels over space-time to produce more distinct features. Each branch accepts the LULC map of 2 years as one of its inputs to determine binary and multiclass changes among the bitemporal images. The STEDSAN model determines the patterns, trends, and conversion from one LULC type to another for the assessment period from 2005 to 2018. The binary change maps were also compared with the existing state of the art (SOTA) CD methods, with STEDSAN having an overall accuracy of 94.93%. The prediction was made using an recurrent neural network (RNN) known as long short term memory network (LSTM) for the year 2025. Experiments were conducted to determine area-wise changes in several LULC classes, such as built-up (BU), crops (kharif crop (KC), rabi crop (RC), zaid crop (ZC), double/triple (D/T C)), current fallow (CF), plantation (PL), forests (evergreen forest (EF), deciduous forest (DF), degraded/scurb forest (D/SF)), littoral swamp (LS), grassland (GL), wasteland (WL), waterbodies max (Wmx), and waterbodies min (Wmn). As per the analysis, over the period of 13 years, there has been a net increase in the amount of BU (1.25%), RC (1.17%), and D/TC(2.42%) and a net decrease in DF (3.29%) and WL(1.44%) being the most dominant classes being changed. These findings will offer a thorough description of identifying trends in coastal areas that may incorporate methodological hints for future studies. This study will also promote handling the spatial and temporal complexity of remotely sensed data employed in categorizing the coastal LULC of a heterogeneous landscape. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.
Attention-Based Bitemporal Image Deep Feature-Level Change Detection for High Resolution Imagery
(Springer Science and Business Media Deutschland GmbH, 2023) Naik, N.; Chandrasekaran, K.; Venkatesan, M.; Prabhavathy, P.
To understand the intricacy of changes on the surface of the land, change detection is an important field in the area of remote sensing. Bitemporal remote sensing images are resourceful information to perform the analysis related to classification and change detection. Most of the architectures proposed for improving the performance of change detection in high resolution images pose a challenge due to composite texture features and finer image details. In this paper, we propose a change detection approach for bitemporal images using supervised learning. Firstly, extraction of the features is performed using a pretrained neural network. Then, the extracted features are provided to a (DSDEN) deep supervised-based difference evaluation network. Then, channel and spatial-based attention components are incorporated for fusing the difference image features with the deep features of raw images for the reconstruction of the final change map. The experimental evaluation on public â€œLEVIR-CDâ€ dataset demonstrates the effectiveness and superiority over traditional methods. Â© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Directional synthesis of aviation-, diesel-, and gasoline range hydrocarbon fuels by catalytic transformations of biomass components: An overview
(Elsevier Ltd, 2023) Dutta, S.; Madav, V.; Joshi, G.; Naik, N.; Kumar, S.
Selective conversion of heavily oxygenated biomolecules into hydrocarbon-based liquid transportation fuels with stipulated structural traits is of academic and industrial significance. This work provides an overview of producing fuel precursors from biomass components and their catalytic transformation into aviation-, diesel-, and gasoline-range hydrocarbon fuels (HCFs). Strategic applications of various organic transformations for the molecular design of targeted products have been rationalized. Construction and alteration of the carbon skeletal system in the fuel candidates via chemical-catalytic transformations have been highlighted. Emphasis has also been given to the process conditions and details of the catalysts employed in these processes. Critical analysis of the literature data presented in this review will assist the researchers in developing more proficient processes for the biorenewable production of drop-in HCFs. © 2023 Elsevier Ltd
Dual attention guided deep encoder-decoder network for change analysis in land use/land cover for Dakshina Kannada District, Karnataka, India
(Springer Science and Business Media Deutschland GmbH, 2023) Naik, N.; Chandrasekaran, K.; Sundaram, V.M.; Prabhavathy, P.
The Earth is frequently changed by natural occurrences and human actions that have threatened our environment to a certain extent. Therefore, accurate and timely monitoring of transformations at the surface of the Earth is crucial for precisely facing their harmful effects and consequences. This paper aims to perform a change detection (CD) analysis and assessment of the Dakshina Kannada region, being one of the coastal districts of Karnataka, India. The spatial and temporal variations in land use and land cover (LULC) are being monitored and examined from the data received as LULC maps from the National Remote Sensing Agency, Indian Space Research Organization, India. The time-series data from advanced wide-field sensor (AWiFS) Resourcesat2 satellite as LULC maps (1:250k) are analyzed using a deep learning approach with an encoder–decoder architecture with dual-attention modules for the change analysis. The model provides an overall accuracy and meanIOU(intersection over union) of 94.11% and 74.1%. The LULC maps from 2005 to 2018 (13 years) are utilized to decide the variations in the LULC, including urban development, agricultural variations, vegetation dynamics, forest areas, barren land, littoral swamp, and water bodies, current fallow, etc. The multiclass area-wise changes in terms of percentage show a decline in most LULC classes, which raises a point of concern for the environmental safety of the considered area, which is highly exposed to coastal flooding due to increased urbanization. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Dynamic force modelling and experimental analysis of reaming
(Engineered Science Publisher, 2021) Kamath, C.R.; Bekinal, S.I.; Bhat, R.; Naik, N.; Kuttan, A.
The production Reaming process plays a vital role in several applications, ranging from automotive to medical sectors. It is performed to enlarge the pre-drilled hole to obtain its required diameter within the specified tolerance limits. The typical operational faults found in the reaming process significantly contribute to damage in the final hole quality. Thus, a dynamic force model is developed in the present work to predict the cutting forces developed during the reaming process. The inputs to the model are broadly classified into tool geometry and vibration system elements. The cutting forces acting in all three directions during the reaming are predicted. The double modulation principle is applied to develop the dynamic force model for computing the cutting forces in the reaming process. The dynamic force model thus developed and simulated using MATLAB® R2019b is examined and validated through actual experiments for no fault conditions. The results obtained infer a high degree of fitness between the values obtained from the developed mechanistic model and the experimental values with a prediction error of less than 5%. © 1999. The American Astronomical Society. All rights reserved.
Forecasting Land-Use and Land-Cover Change Using Hybrid CNN-LSTM Model
(Institute of Electrical and Electronics Engineers Inc., 2024) Varma, B.; Naik, N.; Chandrasekaran, K.; Venkatesan, M.; Rajan, J.
Land-use and land-cover (LULC) information helps analyze future trends and is essential for environmental management and sustainable planning. Time-series satellite images are employed in this study to forecast changes in LULC. Deep-learning (DL) frameworks have been widely used for modeling dynamic LULC changes at the regional level. However, improving the accuracy of the existing prediction models is necessary. This letter proposes an integrated convolutional neural network (CNN) and long short-term memory network (LSTM) known as a hybrid CNN-LSTM model to address the fine-scale LULC prediction requirement. The efficiency of the proposed approach was examined using LULC data for the Dakshina Kannada District of Karnataka State, India. The proposed model achieved an overall accuracy of 95.11% and a kappa coefficient of 0.92, based on the ground-truth data for 2014. The model's predictions for 2035, based on data from 2005 to 2014, revealed the following trends: Urbanization exhibited a pattern of rapid expansion and increased growth. The integrated CNN-LSTM model extracted spatial and temporal features for effectively predicting LULC changes. Infrastructure development, population density, and enhanced economic activities were the major driving factors of changes in LULC for the study region. Robust LULC change forecasting will strengthen LULC evaluations, aid in understanding complex land-use systems, and empower decision-makers to formulate effective land management strategies in the coming years. © 2004-2012 IEEE.
GARCH Model Identification for Stock Crises Events
(Elsevier B.V., 2020) Naik, N.; Mohan, B.R.; Jha, R.A.
The stock market crash is a sudden dramatic decline of stock price due to uncertainty in the stock market. The stock prices are the influence of many factors, such as global trends, local trends, and economic conditions. The identification stock crisis is a challenging task for stock traders and investors. The goal of this paper is to forecast stock crisis events. The experiment is carried out in two steps. First is the least square (LS) method, and the least absolute deviation (LAD) is considered to identify a correlation between mean and median. Based on the correlation between mean and median, the GARCH (General autoregression conditional heteroskedasticity) model proposed to calculate the error distribution in stock returns. To identify the appropriate error distribution, we have varied the degree of t distribution parameters. In the second step, the volatility of stock prices is given as input to the GARCH model to forecast the future crisis events. To carry out the proposed experiment, we have considered Infosys and sbi stock. Experiment results reduce the error in predicting stock crises events. Â© 2020 The Authors. Published by Elsevier B.V.
GARCH-Model Identification based on Performance of Information Criteria
(Elsevier B.V., 2020) Naik, N.; Mohan, B.R.; Jha, R.A.
The stock market prices are volatile due to influence by many factors such as global trends, local trends, and economic conditions. Identification of Generalized autoregressive conditional heteroscedasticity(GARCH) order for stock data is a challenging task due to more fluctuation in stock prices and high variance in data. GARCH is considered to model the conditional volatility of a stock time series. Stock markets data often exhibit volatility clustering. Though many models which belong to autoregressive conditional heteroscedasticity (ARCH) family has proposed, but all the previous studies gave their affirmative consent on the performance of GARCH (1,1), which is considered the standard model, maybe because of the belief held by many researchers that the first lag of conditional variance can capture all the volatility clustering. This can be highly misguiding, especially when the stock market data has high order variance. The focus of this work is to make use of existing, well-known Information Criteria (IC) to identify the stock indices data-generating-process whenever the GARCH effect is present. Akaike Informations Criteria (AIC), Bayesian Information Criteria(BIC), and Hannan-Quinn information(HQ) criteria have used for this experiment. We studied different models with different parameter values and observed the abilities of information criterion in choosing the correct model from a given pool of models. For higher-order GARCH models and high sample sizes, AIC was able to correctly predict the model with high probability, while BIC and HQ performed well for smaller order models. Â© 2020 The Authors. Published by Elsevier B.V.
Hybrid Model of Multifactor Analysis with RNN-LSTM to Predict Stock Price
(Springer Science and Business Media Deutschland GmbH, 2022) Singh, N.; Mohan, B.R.; Naik, N.
Prediction on the stock market is one of the most difficult tasks to do in real life. There are so many aspects on which the stock market dependsâ€”physical factors versus psychological, rational, and irrational behavior, etc. Proposed research work consists of different aspects on which stock markets are based on. It consists of three models to forecast a stock price on State Bank of India (SBI) stock data. In the current research, we proposed a hybrid model followed by recurrent neural network-long short-term memory (RNN-LSTM) to predict a next-day closing price of SBI. A hybrid model is the combination of two different aspects related to the prediction of stock price. The first technique used other companiesâ€™ stock data to predict the target companyâ€™s next-day closing price. Other companies lie in the same sector so that they are correlated to each other. For training and testing, we have used multilayer perceptron (MLP) regression model. It is a neural network model in deep learning. The second technique is to predict the stock price of an SBI company using historical data of the target company followed by the auto-regressive integrated moving averageâ€”gated recurrent unit (ARIMA-GRU) model. ARIMA-GRU model is a combined model which gives better accuracy for predicting stock price data. In the hybrid model, we take the result of both the models as an input. This paper aims to compare the proposed hybrid model with other two single-aspect models on which stock price depends and proves in terms of accuracy that the hybrid model of all aspects gives better results in comparison to single-aspect models. Â© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Intraday Stock Prediction Based on Deep Neural Network
(Springer, 2020) Naik, N.; Mohan, B.R.
Predicting stock price movements is difficult due to the speculative nature of the stock market.Accurate predictions of stock prices allow traders to increase their profits. Stock prices react when receiving new information.During the trading day, it is difficult to understand the up and down movements signaled by stock prices. This paper addresses the problem of fluctuations in stock prices. We proposed the method to identify stock movement trend in data, and this method considered the combination of candlestick data and technical indicator values. The outcome of this method is given as inputs to a deep neural network (DNN) to classify a stock price’s up and down movements. National Stock Exchange, India, datasets are considered for an experiment from the years 2008 to 2018. The work is carried out using H2O deep learning on an RStudio platform. Experimental results are compared with a three-layer artificial neural network (ANN) model. The proposed five-layer DNN model outperforms state-of-the-art methods by 8–11% in predicting up and down movements of a given stock. © 2019, The National Academy of Sciences, India.
Log Periodic Power Law Fitting on Indian Stock Market
(Springer, 2020) Naik, N.; Mohan, B.R.
Stock price prediction is one of the challenging tasks for researchers and academics due to frequent changes in stock prices. The stock prices are speculation, and it purely depends on the demand and supply of the market during the trading session. Most of the existing work approach is foresting stock prices using machine learning methods. There has been a limited number of studies on stock crisis identification. Log periodic power law (LPPL) is one of the approaches to identify bubbles in the stock market before crises happened. By looking at existing work, we found that LPPL has not applied in the Indian stock market. In this paper, we have considered LPPL to identify a bubble in the Indian stock market. Due to fluctuation in the market, stock price follows the nonlinearity behavior, hence LPPL is considered to fit the equations. The experiment is carried out R Studio platform. Â© 2020, Springer Nature Singapore Pte Ltd.
Novel Stock Crisis Prediction Technique - A Study on Indian Stock Market
(Institute of Electrical and Electronics Engineers Inc., 2021) Naik, N.; Mohan, B.R.
A stock market crash is a drop in stock prices more than 10% across the major indices. Stock crisis prediction is a difficult task due to more volatility in the stock market. Stock price sell-offs are due to various reasons such as company earnings, geopolitical tension, financial crisis, and pandemic situations. Crisis prediction is a challenging task for researchers and investors. We proposed a stock crisis prediction model based on the Hybrid Feature Selection (HFS) technique. First, we proposed the HFS algorithm to removes the irrelevant financial parameters features of stock. The second is the Naive Bayes method is considered to classify the strong fundamental stock. The third is we have used the Relative Strength Index (RSI) method to find a bubble in stock price. The fourth is we have used moving average statistics to identify the crisis point in stock prices. The fifth is stock crisis prediction based on Extreme Gradient Boosting (XGBoost) and Deep Neural Network (DNN) regression method. The performance of the model is evaluated based on Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error(RMSE). HFS based XGBoost method was performed better than HFS based DNN method for predicting the stock crisis. The experiments considered the Indian datasets to carry out the task. In the future, the researchers can explore other technical indicators to predict the crisis point. There is more scope to improve and fine-tune the XGBoost method with a different optimizer. © 2013 IEEE.
Optimal Feature Selection of Technical Indicator and Stock Prediction Using Machine Learning Technique
(2019) Naik, N.; Mohan, B.R.
Short-term trading is a difficult task due to fluctuating demand and supply in the stock market. These demands and supply are reflected in stock prices. The stock prices may be predicted using technical indicators. Most of the existing literature considered the limited technical indicators to measure short-term prices. We have considered 33 different combinations of technical indicators to predict the stock prices. The paper has two objectives, first is the technical indicator feature selection and identification of the relevant technical indicators by using Boruta feature selection technique. The second objective is an accurate prediction model for stocks. To predict stock prices we have proposed ANN (Artificial Neural Network) Regression prediction model and model performance is evaluated using metrics is Mean absolute error (MAE) and Root mean square error (RMSE). The experimental results are better than the existing method by decreasing the error rate in the prediction to 12%. We have used the National Stock Exchange, India (NSE) data for the experiment. � 2019, Springer Nature Singapore Pte Ltd.
Optimal Feature Selection of Technical Indicator and Stock Prediction Using Machine Learning Technique
(Springer Verlag service@springer.de, 2019) Naik, N.; Mohan, B.R.
Short-term trading is a difficult task due to fluctuating demand and supply in the stock market. These demands and supply are reflected in stock prices. The stock prices may be predicted using technical indicators. Most of the existing literature considered the limited technical indicators to measure short-term prices. We have considered 33 different combinations of technical indicators to predict the stock prices. The paper has two objectives, first is the technical indicator feature selection and identification of the relevant technical indicators by using Boruta feature selection technique. The second objective is an accurate prediction model for stocks. To predict stock prices we have proposed ANN (Artificial Neural Network) Regression prediction model and model performance is evaluated using metrics is Mean absolute error (MAE) and Root mean square error (RMSE). The experimental results are better than the existing method by decreasing the error rate in the prediction to 12%. We have used the National Stock Exchange, India (NSE) data for the experiment. Â© 2019, Springer Nature Singapore Pte Ltd.
Processing, Mechanical Characterization, and Electric Discharge Machining of Stir Cast and Spray Forming-Based Al-Si Alloy Reinforced with ZrO2 Particulate Composites
(MDPI, 2022) Shetty, R.; Gurupur, P.R.; Hindi, J.; Hegde, A.; Naik, N.; Ali, M.S.S.; Patil, I.S.; Nayak, M.
High performance lightweight structures made of metal matrix composites (MMCs) are in demand for application in variety of industries such as aircraft, spacecraft, automobile, marine, sports equipment, etc. However, uniform distribution of the reinforcement phase to improve the mechanical properties and quality of MMCs has been the challenge for the manufacturing industries. Hence, researchers are focusing on the development of traditional low-cost method of producing metal matrix composites. In the view of above facts, an attempt is made to study the processing and characterization of Si-Al alloy reinforced with zirconium dioxide particulate composites in this paper. Hence, this paper concentrates on experimentally identifying the effect of stir cast and spray forming processing techniques followed by hot pressing on micro hardness, compressive strength, and tensile strength using Taguchi’s design of experiments for aluminum silicon matrix alloy reinforced with zirconium dioxide particulates. From the extensive experimentation on aluminum and silicon reinforced with the ZrO2 powder particulates, it was observed that there was an improvement in selected mechanical properties as the percentage of ZrO2 increased with 13 wt.% of silicon under spray forming processing technique compared to stir cast composites. This may be due to uniform distribution homogenous dispersion, larger work hardening rate, and structure of dislocation tangles around the ZrO2 particulates that occurred during spray forming processing technique. Further, results obtained from the interaction plot, contour plot, main effects plot, and analysis of variance (ANOVA) proved to be successful for identifying the optimum processing parameters for Si-Al alloy reinforced with zirconium dioxide particulate composites. Further, this paper also discusses wear study using pin on disc wear testing apparatus on spray forming processed aluminum and silicon (13.0 wt.%) alloy reinforced with the ZrO2 powder particulates based on Taguchi’s design of experiments followed by second order model generation for wear using response surface methodology. Finally, electrode wear study of spray forming processed aluminum and silicon alloy reinforced with the ZrO2 powder particulates using electric discharge machining by varying peak current (A), pulse on time (μs), and pulse off time (μs) using brass, copper, and graphite as electrode material based on L27orthogonal array. The understanding gained from the design of experiments in this paper can be used to develop future guidelines for processing and characterization of Si-Al alloy reinforced with zirconium dioxide particulate composites. © 2022 by the authors.
Spatio-temporal analysis of land use/land cover change detection in small regions using self-supervised lightweight deep learning
(Springer Science and Business Media Deutschland GmbH, 2023) Naik, N.; Chandrasekaran, K.; Sundaram, V.; Prabhavathy, P.
Change detection (CD) has sparked a lot of scientific interest in recent decades as one of the core concerns in Earth observation. The enhancement of the CD source data with the availability of multitemporal images with varying resolutions provides ample change indicators due to the rapid improvement of satellite sensors. However, precisely detecting real changed locations continues to be a complicated task. CD from remote sensing images (RSI) becomes challenging when the labeled data for supervised learning is unavailable. This article proposes a novel CD framework using a self-supervised learning (SSL) approach to overcome these limitations. First, the superpixel segmentation method of simple linear iterative clustering (SLIC) using a structural similarity index is incorporated to produce a difference image (DI). The change features are extracted to represent the difference information using spatial features between the corresponding superpixels. Second, a parallel clustering algorithm called fuzzy C-means (FCM) separates the DI into three clusters of changed, unchanged, and intermediate classes. The image patches of changed, unchanged and intermediate classes are constructed as training and testing samples. A lightweight deep convolutional neural network (LWDCNN) is trained with the training samples to detect the semantic difference and classify the testing samples into the changed or unchanged class. Finally, merging intermediate and change class labels generates a robust and high-contrast CD map. Numerical experiments were performed on two small regions like the Alappuzha, Kerala, India, and Paris building dataset to demonstrate the usefulness of the proposed approach, achieving an overall accuracy of 98.28% and 96.43% for determining changes effectively. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Stock price movements classification using machine and deep learning techniques-the case study of indian stock market
(2019) Naik, N.; Mohan, B.R.
Stock price movements forecasting is an important topic for traders and stock analyst. Timely prediction in stock yields can get more profits and returns. The predicting stock price movement on a daily basis is a difficult task due to more ups and down in the financial market. Therefore, there is a need for a more powerful predictive model to predict the stock prices. Most of the existing work is based on machine learning techniques and considered very few technical indicators to predict the stock prices. In this paper, we have extracted 33 technical indicators based on daily stock price such as open, high, low and close price. This paper addresses the two problems, first is the technical indicator feature selection and identification of the relevant technical indicators by using Boruta feature selection technique. The second is an accurate prediction model for stock price movements. To predict stock price movements we have proposed machine learning techniques and deep learning based model. The performance of the deep learning model is better than the machine learning techniques. The experimental results are significant improves the classification accuracy rate by 5% to 6%. National Stock Exchange, India (NSE) stocks are considered for the experiment. � Springer Nature Switzerland AG 2019.
Stock price movements classification using machine and deep learning techniques-the case study of indian stock market
(Springer Verlag service@springer.de, 2019) Naik, N.; Mohan, B.R.
Stock price movements forecasting is an important topic for traders and stock analyst. Timely prediction in stock yields can get more profits and returns. The predicting stock price movement on a daily basis is a difficult task due to more ups and down in the financial market. Therefore, there is a need for a more powerful predictive model to predict the stock prices. Most of the existing work is based on machine learning techniques and considered very few technical indicators to predict the stock prices. In this paper, we have extracted 33 technical indicators based on daily stock price such as open, high, low and close price. This paper addresses the two problems, first is the technical indicator feature selection and identification of the relevant technical indicators by using Boruta feature selection technique. The second is an accurate prediction model for stock price movements. To predict stock price movements we have proposed machine learning techniques and deep learning based model. The performance of the deep learning model is better than the machine learning techniques. The experimental results are significant improves the classification accuracy rate by 5% to 6%. National Stock Exchange, India (NSE) stocks are considered for the experiment. Â© Springer Nature Switzerland AG 2019.
Stock price volatility estimation using regime switching technique-empirical study on the indian stock market
(MDPI AG, 2021) Naik, N.; Mohan, B.R.
Volatility is the degree of variation in the stock price over time. The stock price is volatile due to many factors, such as demand, supply, economic policy, and company earnings. Investing in a volatile market is riskier for stock traders. Most of the existing work considered Generalized Auto-regressive Conditional Heteroskedasticity (GARCH) models to capture volatility, but this model fails to capture when the volatility is very high. This paper aims to estimate the stock price volatility using the Markov regime-switching GARCH (MSGARCH) and SETAR model. The model selection was carried out using the Akaike-Informations-Criteria (AIC) and Bayesian-Information Criteria (BIC) metric. The performance of the model is evaluated using the Root mean square error (RMSE) and mean absolute percentage error (MAPE) metric. We have found that volatility estimation using the MSGARCH model performed better than the SETAR model. The experiments considered the Indian stock market data. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.