<p>Accurate stock price prediction is significant for investors to avoid risks and improve the return on investment. Stock a typical nonlinear time-series problem, which many factors affect. Still, too much analysis of influencing will lead input redundancy large amount computation in model. Although model based Recurrent Neural Network (RNN) has good effect, it problem oversaturation. Th...

Abstract: 
 Recently, there have been studies on the use of machine learning algorithms for price prediction in many different areas such as stock market, rent a house and used car sales. Studies give information about which algorithm is more successful using methods. The most commonly method linear regression model. In our study, we examined effectiveness model prediction. applied data se...

Stock market price prediction is difficult and complex task. Prediction in stock very unstable Process. Price are most of the time tend to follow patterns those more or less regular curve. Machine Learning techniques use different predictive models algorithms predict automate things reduce human effort. This research paper focuses on Long Short Term Memory (LSTM) future company using each day c...

In general, forecasting on stock prices is a famous and interesting area that gathers many researchers in. Contemporarily, after the birth of AI, number algorithms used in prediction equity market fluctuation are boomed rapidly. Applying combination statistics can help as well investors learn about either short-term regulation (such opening price) or long-term movement. This paper discusses thr...

We consider a prediction market in which traders have heterogeneous prior beliefs in probabilities. In the two-state case, we derive necessary and sufficient conditions so that the prediction market is accurate in the sense that the equilibrium state price equals the mean probabilities of traders’ beliefs. We also provide a necessary and sufficient condition for the well documented favorite-lon...

Metal binding is important for the structural and functional characterization of proteins. Previous prediction efforts have only focused on bonding state, i.e. deciding which protein residues act as metal ligands in some binding site. Identifying the geometry of metal-binding sites, i.e. deciding which residues are jointly involved in the coordination of a metal ion is a new prediction problem ...

It is difficult to find out which is more effective and accurate method for stock rate prediction so that a buy or sell signal can be generated for given stocks. This paper presents a number of technical indicators and Back Propagation Neural Network to predict the stock price of the day. Stock rate prediction accuracy of different technical indicators and backpropagation neural network has bee...

Crude oil pricing is commonly expressed as a formula referenced to Brent or WTI crude oil. The final price of these two qualities and the spread between WTI and Brent can drive the decision when the purchase of a crude oil cargo is evaluated. A crude oil price-forecasting model is presented. It is based on past data, inventory level and volatility index and it is derived with a neuro fuzzy infe...