Big data promises automated actionable knowledge creation and predictive models for use by both humans and computers.

/pdf/data-science-and-prediction-nbwcwga390.pdf

Data science and prediction

The use of the term 'Data Science' is becoming increasingly common along with 'Big Data.' What does Data Science mean? Is there something unique about it? What skills should a 'data scientist' possess to be productive in the emerging digital age characterized by a deluge of data? What are the implications for business and for scientific inquiry? In this brief monograph I address these questions from a predictive modeling perspective.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID2086734_code402993.pdf?abstractid=2086734&mirid=1&type=2

Data Science and Prediction

This essay discusses the use of big data analytics (BDA) as a strategy of enquiry for advancing information systems (IS) research. In broad terms, we understand BDA as the statistical modelling of ...

/pdf/utilizing-big-data-analytics-for-information-systems-52lbbdve15.pdf

Utilizing big data analytics for information systems research: challenges, promises and guidelines

Neural networks have become standard and important tools for data mining. This chapter provides an overview of neural network models and their applications to data mining tasks. We provide historical development of the field of neural networks and present three important classes of neural models including feedforward multilayer networks, Hopfield networks, and Kohonen’s self-organizing maps. Modeling issues and applications of these models for data mining are discussed.

Neural Networks For Data Mining

Candlestick charts display the high, low, opening, and closing prices in a specific period. Candlestick patterns emerge because human actions and reactions are patterned and continuously replicate. These patterns capture information on the candles. According to Thomas Bulkowski’s Encyclopedia of Candlestick Charts, there are 103 candlestick patterns. Traders use these patterns to determine when to enter and exit. Candlestick pattern classification approaches take the hard work out of visually identifying these patterns. To highlight its capabilities, we propose a two-steps approach to recognize candlestick patterns automatically. The first step uses the Gramian Angular Field (GAF) to encode the time series as different types of images. The second step uses the Convolutional Neural Network (CNN) with the GAF images to learn eight critical kinds of candlestick patterns. In this paper, we call the approach GAF-CNN. In the experiments, our approach can identify the eight types of candlestick patterns with 90.7% average accuracy automatically in real-world data, outperforming the LSTM model.

/pdf/encoding-candlesticks-as-images-for-pattern-classification-1xkydrl53z.pdf

Encoding candlesticks as images for pattern classification using convolutional neural networks

We compare four nonlinear methods on their ability to learn models from data. The problem requires predicting whether a company will deliver an earnings surprise a specific number of days prior to announcement. This problem has been well studied in the literature using linear models. A basic question is whether machine learning-based nonlinear models such as tree induction algorithms, neural networks, naive Bayesian learning, and genetic algorithms perform better in terms of predictive accuracy and in uncovering interesting relationships among problem variables. Equally importantly, if these alternative approaches perform better, why? And how do they stack up relative to each other? The answers to these questions are significant for predictive modeling in the financial arena, and in general for problem domains characterized by significant nonlinearities. In this paper, we compare the four above-mentioned nonlinear methods along a number of criteria. The genetic algorithm turns out to have some advantages in finding multiple "small disjunct" patterns that can be accurate and collectively capable of making predictions more often than its competitors. We use some of the nonlinearities we discovered about the problem domain to explain these results.

/pdf/a-comparison-of-nonlinear-methods-for-predicting-earnings-7bfqh116uh.pdf

A comparison of nonlinear methods for predicting earnings surprises and returns

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A comparison of nonlinear methods for predicting earnings surprises and returns