In this study, a multiscale neural network learning paradigm based on empirical mode decomposition (EMD) is proposed for crude oil price prediction. In this learning paradigm, the original price series are first decomposed into various independent intrinsic mode components (IMCs) with a range of frequency scales. Then the internal correlation structures of different IMCs are explored by neural ...

Housing sale price prediction has been extensively studied under semiparametric regression models. However, semiparametric kernel machines with spatial effect term have not been studied yet. This paper proposes semiparametric spatial effect kernel minimum squared error model (SSEKMSEM) and least squares support vector machine (SSELS-SVM) for estimating a hedonic price function and compares the ...

The Agricultural sector needs more support for its development in developing countries like India. Price prediction helps the farmers and also the Government to make effective decision. Based on the complexity of vegetable price prediction, making use of the classification technique like neural networks such as self build up the model of Back-propagation neural network (BPNN) to predict vegetab...

Our time is the era of machine learning. To make work easier, every profession implementing learning techniques. It difficult to predict stock market since it needs in-depth knowledge how ignore news events, assess past data, and determine events affect price trends. The difficulty made more by erratic prices are. A fair in result prophecy equalizing sales. goal prediction forecast value a comp...

Abstract -Stocks have been the centre of world economic for around more than a century. The basic needs man are food, water and shelter but dynamics how these met changes with time. One most important aspects is economics mankind. wars, dot com crash cold war had great impact on stocks. next big certainly going to be that Artificial Intelligence. Key Words: LSTM, RNN, Normalization algorithm, e...

Stock price crash risk has a significant impact on investors, creditors, managers, and shareholders, so the prediction of this phenomenon is a very important issue in investment and risk management decisions. This research investigates the effect of business strategy and stock price synchronicity on stock price crash risk. Following Bentley et al.[2], composite strategy score has been used to ...

How to analyze the features of stock price accurately and master the regularity of stock price changing with time quickly and effectively is of great theoretical and realistic significance and is an important research direction in financial field. For complicated non-linear and periodic variations of stock prices, a parallel computing model is proposed in this paper based on stock prediction al...