Forecasting Outlier Occurrence in Stock Market Time Series Based on Wavelet Transform and Adaptive ELM Algorithm

Hosseinioun, Nargess

doi:10.4236/jmf.2016.61013

Cited by 12 publications

(4 citation statements)

References 18 publications

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“…Adaptive Extreme Learning Machine (AELM) was used to predict outliers indicating the volatility of stock prices using Wavelet Transform (WT) features [12]. AELM is an improved training algorithm for Artificial Neural Networks (ANN), which can train the ANN with minimal parameter settings.…”

Section: B Ai-based Methods For Forecasting Stock Marketsmentioning

confidence: 99%

Multi-Layer Perceptron (MLP)-Based Nonlinear Auto-Regressive with Exogenous Inputs (NARX) Stock Forecasting Model

Yassin¹,

Herman²,

Pasya³

et al. 2017

International Journal on Advanced Science, Engineering and Information Technology

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The prediction of stocks in the stock market is important in investment as it would help the investor to time buy and sell transactions to maximize profits. In this paper, a Multi-Layer Perceptron (MLP)-based Nonlinear Auto-Regressive with Exogenous Inputs (NARX) model was used to predict the prices of the Apple Inc. weekly stock prices over a time horizon of 1995 to 2013. The NARX model is a system identification model that constructs a mathematical model from the dynamic input/output readings of the system and predicts the future behaviour of the system based on the constructed mathematical model. The One Step Ahead (OSA) and correlation tests were used to validate the model. Results demonstrate the predictive ability of the model while producing Gaussian residuals (indicating the validity of the model).

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Section: B Ai-based Methods For Forecasting Stock Marketsmentioning

confidence: 99%

Multi-Layer Perceptron (MLP)-Based Nonlinear Auto-Regressive with Exogenous Inputs (NARX) Stock Forecasting Model

Yassin¹,

Herman²,

Pasya³

et al. 2017

International Journal on Advanced Science, Engineering and Information Technology

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“…Their methodology is more data dependent and more suitable for data detection in the process of unstable regulation. (Hosseinioun, 2016), considered an adaptive ensemble models of Extreme Learning Machines (ELMs.) combined with wavelet transform to forecast the stock market index value outliers.…”

Section: Wavelets and Outlier Detectionsmentioning

confidence: 99%

Outlier Detection Based on Discrete Wavelet Transform with Application to Saudi Stock Market Closed Price Series

Rashedi¹,

Ismail²,

Wadi³

et al. 2020

JAFEB

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This study investigates the problem of outlier detection based on discrete wavelet transform in the context of time series data where the identification and treatment of outliers constitute an important component. An outlier is defined as a data point that deviates so much from the rest of observations within a data sample. In this work we focus on the application of the traditional method suggested by Tukey (1977) for detecting outliers in the closed price series of the Saudi Arabia stock market (Tadawul) between Oct. 2011 and Dec. 2019. The method is applied to the details obtained from the MODWT (Maximal-Overlap Discrete Wavelet Transform) of the original series. The result show that the suggested methodology was successful in detecting all of the outliers in the series. The findings of this study suggest that we can model and forecast the volatility of returns from the reconstructed series without outliers using GARCH models. The estimated GARCH volatility model was compared to other asymmetric GARCH models using standard forecast error metrics. It is found that the performance of the standard GARCH model were as good as that of the gjrGARCH model over the out-of-sample forecasts for returns among other GARCH specifications.

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“…It has been verified that ELM costs much less training time and has better or similar generalization performance than SVM and traditional neural networks [17]. Hosseinioun presented the use of wavelet transform and adaptive ELM to forecast outlier occurrence in stock market time series [18]. Dash presented an optimized ELM for predicting financial time series [19].…”

Section: Introductionmentioning

confidence: 99%

Time Series Prediction Based on Adaptive Weight Online Sequential Extreme Learning Machine

Huang

Lü

2017

Applied Sciences

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A novel adaptive weight online sequential extreme learning machine (AWOS-ELM) is proposed for predicting time series problems based on an online sequential extreme learning machine (OS-ELM) in this paper. In real-world online applications, the sequentially coming data chunk usually possesses varying confidence coefficients, and the data chunk with a low confidence coefficient tends to mislead the subsequent training process. The proposed AWOS-ELM can improve the training process by accessing the confidence coefficient adaptively and determining the training weight accordingly. Experiments on six time series prediction data sets have verified that the AWOS-ELM algorithm performs better in generalization performance, stability, and prediction ability than the OS-ELM algorithm. In addition, a real-world mechanical system identification problem is considered to test the feasibility and efficacy of the AWOS-ELM algorithm.

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Forecasting Outlier Occurrence in Stock Market Time Series Based on Wavelet Transform and Adaptive ELM Algorithm

Cited by 12 publications

References 18 publications

Multi-Layer Perceptron (MLP)-Based Nonlinear Auto-Regressive with Exogenous Inputs (NARX) Stock Forecasting Model

Multi-Layer Perceptron (MLP)-Based Nonlinear Auto-Regressive with Exogenous Inputs (NARX) Stock Forecasting Model

Outlier Detection Based on Discrete Wavelet Transform with Application to Saudi Stock Market Closed Price Series

Time Series Prediction Based on Adaptive Weight Online Sequential Extreme Learning Machine

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