Stock values predictions using deep learning based hybrid models

Abstract

Predicting the correct values of stock prices in fast fluctuating high-frequency financial data is always a challenging task. A deep learning-based model for live predictions of stock values is aimed to be developed here. The authors' have proposed two models for different applications. The first one is based on Fast Recurrent Neural Networks (Fast RNNs). This model is used for stock price predictions for the first time in this work. The second model is a hybrid deep learning model developed by utilising the best features of FastRNNs, Convolutional Neural Networks, and Bi-Directional Long Short Term Memory models to predict abrupt changes in the stock prices of a company. The 1-min time interval stock data of four companies for a period of one and three days is considered. Along with the lower Root Mean Squared Error (RMSE), the proposed models have low computational complexity as well, so that they can also be used for live predictions. The models' performance is measured by the RMSE along with computation time. The model outperforms Auto Regressive Integrated Moving Average, FBProphet, LSTM, and other proposed hybrid models on both RMSE and computation time for live predictions of stock values. a. Short-term forecasting, where prediction happens for the next second, minute, hour, day, or month b. Medium-term forecasting, where prediction happens for the next 1 year, or 2 years. c. Long-term forecasting, where prediction happens beyond 2 years.

[Retracted] Feature Importance Score‐Based Functional Link Artificial Neural Networks for Breast Cancer Classification

“…In addition, various researchers have worked on different ML techniques to classify breast cancer types. It is found that the existing models suffer from the gradient vanishing [ 22 – 24 ], overfitting [ 25 , 26 ], and data leakage [ 27 , 28 ] kind of problems. Even development of generalized model [34,35] is still defined as an ill-posed problem.…”

A Hybrid Data Analytics Framework with Sentiment Convergence and Multi-Feature Fusion for Stock Trend Prediction

“…The pooling layer takes samples from the feature maps and extracts the most important features in each feature map to pass to the fully connected layer. Finally, the fully connected layer obtains the final classification result of the headlines using the SoftMax function [12] and outputs the event type of the headlines. The daily news about a particular stock is counted by the CNN news classifier.…”

“…The empirical results of news event classification are shown in Table 5. To verify the performance of the CNN-based news event classifier, it was compared with the SVM and maximum entropy methods [9][10][11][12] for news event classification. SVM is widely investigated and applied for modeling, classification, and data-driven error detection as it has been shown to be powerful and generalizable, while maximum entropy is a linear logit model with proven classification capabilities.…”

“…Although stock price movements are stochastic and generally involve non-random events, they can still be predicted by analyzing investor behavior and trading patterns [12][13][14]. While econometrics-based statistical methods can rely on tentative premises, machine learning methods pose challenges due to limited interpretability, the need for manual feature selection, and the problem of overfitting.…”

A Novel Encoder-Decoder Model for Multivariate Time Series Forecasting

“…The former is called univariate time series forecasting; the latter is called multivariate time series forecasting. For example, economists utilized the historical data of stock prices to forecast stock prices or trends [ 5 ], medical scientists made use of the biological time data to predict diseases [ 6 ], transportation departments explored the historical data of traffic flow to predict congestion [ 7 ], and environmentalists employed atmospheric timing data to estimate environmental climate changes [ 8 ], etc. Nevertheless, time series data not only contains abundant information but also appears to some complex characteristics such as high dimension, nonlinear, fluctuation, and spatiotemporal dependence, which make accurate time series data prediction become a challenging study hotspot [ 9 ].…”