Predicting the Daily Return Direction of the Stock Market using Hybrid Machine Learning Algorithms

Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields, including stock market investment. However, few studies have focused on forecasting daily stock market returns, especially when using powerful machine learning techniques, such as deep neural networks (DNNs), to perform the analyses. DNNs employ various deep learning algorithms based on the combination of network structure, activation function, and model parameters, with their performance depending on the format of the data representation. This paper presents a comprehensive big data analytics process to predict the daily return direction of the SPDR S&P 500 ETF (ticker symbol: SPY) based on 60 financial and economic features. DNNs and traditional artificial neural networks (ANNs) are then deployed over the entire preprocessed but untransformed dataset, along with two datasets transformed via principal component analysis (PCA), to predict the daily direction of future stock market index returns. While controlling for overfitting, a pattern for the classification accuracy of the DNNs is detected and demonstrated as the number of the hidden layers increases gradually from 12 to 1000. Moreover, a set of hypothesis testing procedures are implemented on the classification, and the simulation results show that the DNNs using two PCA-represented datasets give significantly higher classification accuracy than those using the entire untransformed dataset, as well as several other hybrid machine learning algorithms. In addition, the trading strategies guided by the DNN classification process based on PCA-represented data perform slightly better than the others tested, including in a comparison against two standard benchmarks

Automated parking space detection

A dissertation submitted to the Faculty of Science, University of the Witwatersrand, in fulfillment of the requirements for the degree of Master of Science, Johannesburg, 2018.Parking space management is a problem that most big cities encounter. Without parking space management strategies, the traffic can become anarchic. Compared to physical sensors around the parking lot, a camera monitoring it can send images to be processed for vacancy detection. This dissertation implements a system to automatically detect and classify spaces (vacant or occupied) in images of a parking lot. Detection is done using the Region based Convolutional Neural Networks (RCNN). It reduces the amount of time that would otherwise be spent manually mapping out a parking lot. After the spaces are detected, they are classified as either vacant or occupied. It is accomplished using the Histograms of Oriented Gradients (HOG) with the Linear and Radial Basis Function (RBF) Support Vector Machines (SVM), Convolutional Neural Networks (CNN) and a Hybrid approach. The classifiers are trained, tested and validated using data collected for this research. We compared the results of the Hybrid classifier against CNN and SVMs. The Hybrid classifier performed better than all the other ones with an accuracy of 89.36% and a precision of 82.54%, which is the best score obtained from all the other classifiers used. Novel contributions of this work include the new labeled database, the use of the RCNN for bay detection, and the classification of bays using the hybrid CNN and SVM.LG201

Combining Stream Mining and Neural Networks for Short Term Delay Prediction

The systems monitoring the location of public transport vehicles rely on wireless transmission. The location readings from GPS-based devices are received with some latency caused by periodical data transmission and temporal problems preventing data transmission. This negatively affects identification of delayed vehicles. The primary objective of the work is to propose short term hybrid delay prediction method. The method relies on adaptive selection of Hoeffding trees, being stream classification technique and multilayer perceptrons. In this way, the hybrid method proposed in this study provides anytime predictions and eliminates the need to collect extensive training data before any predictions can be made. Moreover, the use of neural networks increases the accuracy of the predictions compared with the use of Hoeffding trees only

Machine Learning for Video-Based Event Detection: A CNN-LSTM Model

In recent years, the application of machine learning methodology into event detection has become increasingly prevalent, with examples ranging from surveillance to entertainment and healthcare. This project aims to explore the classification of events in video content with practical implication of content management and archival. To develop a method for event detection, we will utilize the VidLife dataset — a dataset that captures a wide array of life events from the popular American television sitcom series \u27The Big Bang Theory\u27. This project focuses on the development of a hybrid model that combines Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks. To interpret sequential data effectively, we have chosen this combination to capture the spatial and temporal characteristics. The project’s focus is on the challenges involved in accurately identifying and classifying diverse life events in videos, showcasing the potential of machine learning in transforming how we analyze complex video data and explore different applications where automatic video categorization is necessary