Application of Higher-Order Neural Networks to Financial Time-Series Prediction

Fulcher, John; Zhang, Ming; Xu, Shuxiang

doi:10.4018/978-1-59140-670-9.ch005

Cited by 32 publications

(6 citation statements)

References 10 publications

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“…These networks can modify their internal behaviour depending on the results achieved [15]. Traditional areas in which artificial neural networks (ANN) are known to excel are pattern recognition, pattern matching, and mathematical function approximation [16]. An ANN is comprised of processing elements that have inputs from other elements and/or the environment as well as its output.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

A Deep Learning Method for the Detection and Compensation of Outlier Events in Stock Data

Naidoo

Du²

2022

Electronics

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The stock price is a culmination of numerous factors that are not necessarily quantifiable and significantly affected by anomalies. The forecasting accuracy of stock prices is negatively affected by these anomalies. However, very few methods are available for detecting, modelling, and compensating for anomalies in financial time series given the critical roles of better management of funds and accurate forecasting of anomalies. Time series data are a data type that changes over a defined time interval. Each value in the data set has some relation to the previous values in the series. This attribute of time series data allows us to predict the values that will follow in the series. Typical prediction models are limited to following the patterns in the data set without being able to compensate for anomalous periods. This research will attempt to find a machine learning method to detect outliers and then compensate for these detections in the prediction made. This concept was previously unimplemented, and therefore, it will make use of theoretical work on market forecasting, outliers and their effects, and machine learning methods. The ideas implemented in the paper are based upon the efficient market hypothesis (EMH), in which the stock price reflects knowledge about the market. The EMH hypothesis cannot account for consumer sentiment towards a stock. This sentiment could produce anomalies in stock data that have a significant influence on the movement of the stock market. Therefore, the detection and compensation of outliers may improve the predictions made on stock movements. This paper proposes a deep learning method that consists of two sequential stages. The first stage is an outlier detection model based on a long short-term memory (LSTM) network auto-encoder that can determine if an outlier event has occurred and then create an associated value of this occurrence for the next stage. The second stage of the proposed method uses a higher-order neural network (HONN) model to make a prediction based on the output of the first stage and the stock time series data. Real stock data and standalone prediction models are used to validate this method. This method is superior at predicting stock time series data by compensating for outlier events. The improvement is quantifiable if the data set contains an adequate amount of anomalous periods. We may further apply the proposed method of compensating for outliers in combination with other financial time series prediction methods to offer further improvements and stability.

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

A Deep Learning Method for the Detection and Compensation of Outlier Events in Stock Data

Naidoo

Du²

2022

Electronics

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“…Zhang et ai. (2000), Fulcher et al (2006) have developed several different HONN models and these models are tenned as polynomial, trigonometric, and similar HONN models. All HONN model described in their paper utilize various combinations of linear, power, and multiplicative (and sometimes other) neuron types and are trained using standard back-propagation.…”

Section: Types Of Networkmentioning

confidence: 99%

Current Approaches in Neural Network Modeling of Financial Time Series

Singh¹

2009

SSRN Journal

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Neural networks are an artificial intelligence method for modeling complex non-linear functions. Artificial Neural Networks (ANNs) have been widely applied to the domain of prediction problems. Considerable research effort has gone into ANNs for modeling financial time series. This paper attempts to provide an overview of recent research in this area, emphasizing the issues that are particularly important with respect to the neural network approach to financial time series prediction task. The purpose of this paper is to provide (1) a survey of research in this area, and, (2) synthesize insights from published research on ANN modeling issues, specifically, those that have a bearing on the design factors of neural network such as variable selection, data preprocessing, and network architecture. The paptir concludes with a discussion of current and emerging research topics related to neural networks in financial time series prediction.

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“…Methods including Auto-Regressive Moving Average (ARMA) [7], Auto-Regressive Integrated Moving Average (ARIMA) [8], Seasonal ARIMA [9], exponential smoothing [10], and Vector Autoregression (VAR) [11] are parametric models which require prior statistical knowledge on the time series data, which may not be feasible for practical applications. Non-parametric methods for time series modeling and prediction including neural networks [12,13] have been successfully applied to different time series modeling and forecasting applications [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

ML Approach to Improve the Costs and Reliability of a Wireless Sensor Network

Ayanoglu

Uysal

2023

Sensors

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Temperature-controlled closed-loop systems are vital to the transportation of produce. By maintaining specific transportation temperatures and adjusting to environmental factors, these systems delay decomposition. Wireless sensor networks (WSN) can be used to monitor the temperature levels at different locations within these transportation containers and provide feedback to these systems. However, there are a range of unique challenges in WSN implementations, such as the cost of the hardware, implementation difficulties, and the general ruggedness of the environment. This paper presents the novel results of a real-life application, where a sensor network was implemented to monitor the environmental temperatures at different locations inside commercial temperature-controlled shipping containers. The possibility of predicting one or more locations inside the container in the absence or breakdown of a logger placed in that location is explored using combinatorial input–output settings. A total of 1016 machine learning (ML) models are exhaustively trained, tested, and validated in search of the best model and the best combinations to produce a higher prediction result. The statistical correlations between different loggers and logger combinations are studied to identify a systematic approach to finding the optimal setting and placement of loggers under a cost constraint. Our findings suggest that even under different and incrementally higher cost constraints, one can use empirical approaches such as neural networks to predict temperature variations in a location with an absent or failed logger, within a margin of error comparable to the manufacturer-specified sensor accuracy. In fact, the median test accuracy is 1.02 degrees Fahrenheit when using only a single sensor to predict the remaining locations under the assumptions of critical system failure, and drops to as little as 0.8 and 0.65 degrees Fahrenheit when using one or three more sensors in the prediction algorithm. We also demonstrate that, by using correlation coefficients and time series similarity measurements, one can identify the optimal input–output pairs for the prediction algorithm reliably under most instances. For example, discrete time warping can be used to select the best location to place the sensors with a 92% match between the lowest prediction error and the highest similarity sensor with the rest of the group. The findings of this research can be used for power management in sensor batteries, especially for long transportation routes, by alternating standby modes where the temperature data for the OFF sensors are predicted by the ON sensors.

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Application of Higher-Order Neural Networks to Financial Time-Series Prediction

Cited by 32 publications

References 10 publications

A Deep Learning Method for the Detection and Compensation of Outlier Events in Stock Data

A Deep Learning Method for the Detection and Compensation of Outlier Events in Stock Data

Current Approaches in Neural Network Modeling of Financial Time Series

ML Approach to Improve the Costs and Reliability of a Wireless Sensor Network

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