Preliminary Study on Interpreting Stock Price Forecasting Based on Tree Regularization of GRU

Wu, Wenjun; Wang, Yue; Fu, Jiaxiu; Yan, Jiayi; Wang, Zhiping; Liu, Jiaqing; Wang, Weifan; Wang, Xiuli

doi:10.1007/978-981-15-0121-0_37

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…Therefore, there are many research works related to this application area, as described in Ref. 198 In addition, various architectures such as CNN, [124][125][126] DNN, 123 GRU, 122 or LSTM 118,119 have been used. Some authors make a comparison between some of these architectures, analyzing which one offers better results.…”

Section: Hardware Performancementioning

confidence: 99%

Deep Learning for Time Series Forecasting: A Survey

et al. 2021

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Time series forecasting has become a very intensive field of research, which is even increasing in recent years. Deep neural networks have proved to be powerful and are achieving high accuracy in many application fields. For these reasons, they are one of the most widely used methods of machine learning to solve problems dealing with big data nowadays. In this work, the time series forecasting problem is initially formulated along with its mathematical fundamentals. Then, the most common deep learning architectures that are currently being successfully applied to predict time series are described, highlighting their advantages and limitations. Particular attention is given to feed forward networks, recurrent neural networks (including Elman, long-short term memory, gated recurrent units, and bidirectional networks), and convolutional neural networks. Practical aspects, such as the setting of values for hyperparameters and the choice of the most suitable frameworks, for the successful application of deep learning to time series are also provided and discussed. Several fruitful research fields in which the architectures analyzed have obtained a good performance are reviewed. As a result, research gaps have been identified in the literature for several domains of application, thus expecting to inspire new and better forms of knowledge.

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Section: Hardware Performancementioning

confidence: 99%

Deep Learning for Time Series Forecasting: A Survey

et al. 2021

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“…It is shown that GRU using LASSO dimension reduction is better. Wu at al [41] uses GRU with a Tree Regularization technique(GRU-Tree) to forecast the price trend. In this method, a GRU based neural network is converted to a decision tree.…”

Section: Gru Based Modelsmentioning

confidence: 99%

“…σ(t) is what we do not know but has significant impact on the decomposition. As such, EEMD [41] has been put up.…”

Section: Ensemble Empirical Mode Decomposition (Eemd)mentioning

confidence: 99%

Stock Price Prediction Using a Frequency Decomposition Based GRU Transformer Neural Network

Qian

2022

Applied Sciences

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Stock price prediction is crucial but also challenging in any trading system in stock markets. Currently, family of recurrent neural networks (RNNs) have been widely used for stock prediction with many successes. However, difficulties still remain to make RNNs more successful in a cluttered stock market. Specifically, RNNs lack power to retrieve discerning features from a clutter of signals in stock information flow. Making it worse, by RNN a single long time cell from the market is often fused into a single feature, losing all the information about time which is essential for temporal stock prediction. To tackle these two issues, we develop in this paper a novel hybrid neural network for price prediction, which is named frequency decomposition induced gate recurrent unit (GRU) transformer, abbreviated to FDGRU-transformer or FDG-trans). Inspired by the success of frequency decomposition, in FDG-transformer we apply empirical model decomposition to decompose the complete ensemble of cluttered data into a trend component plus several informative and independent mode components. Equipped with the decomposition, FDG-transformer has the capacity to extract the discriminative insights from the cluttered signals. To retain the temporal information in the observed cluttered data, FDG-transformer utilizes hybrid neural network of GRU, long short term memory (LSTM) and multi-head attention (MHA) transformers. The integrated transformer network is capable of encoding the impact of different weights from each past time step to the current one, resulting in the establishment of a time series model from a deeper fine-grained level. We appy the developed FDG-transformer model to analyze Limit Order Book data and compare the results with that obtained from other state-of-the-art methods. The comparison shows that our model delivers effective price forecasting. Moreover, an ablation study is conducted to validate the importance and necessity of each component in the proposed model.

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“…Given their ability to capture non-linear patterns in data, deep learning-based models have been widely employed by researchers for time series prediction tasks. Wu et al [48] proposed GRU-Tree, a decision tree converted from a GRU network, for stock price forecasting. Yang et al [49] introduced HFnet, a multi-branch GRU architecture, for electricity price prediction.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Stock Market Forecasting: A Hybrid Multivariate Approach Incorporating CEEMDAN and Deep Recursive Neural Networks

Ghamgosar,

Alizadeh

2023

Preprint

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Forecasting the future of the stock market is a crucial challenge for companies, financial analysts, policymakers, and other stakeholders. The primary objective is to develop a stable model that can effectively predict stock prices in the face of a volatile financial environment. However, financial time-series forecasting is inherently difficult due to its nonstationary, non-linear, and noisy nature. While various statistical and deep learning models are commonly used for stock price prediction, there is a noticeable lack of research on the effectiveness of different feature pre-processing methods and their combinations for diverse time series. To address this challenge, we propose a hybrid method that incorporates multiple perspectives to achieve optimal results. Our study focuses on financial time-series forecasting in the context of crude oil price fluctuations. The method involves several stages, including feature pre-processing, signal processing, and deep recursive neural networks. Anomalies, representing financial noises, are detected using an unsupervised Autoencoder. Additionally, a data enrichment phase refines the time series using CEEMDAN decomposition and Fourier Transform. Finally, a bi-directional LSTM model is utilized to extract latent patterns and forecast future trends in the time series. This approach aids in the progression of stock market forecasting, imparting valuable perspectives for decision-making among businesses, financial analysts, policymakers, and other individuals with a vested interest.

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Preliminary Study on Interpreting Stock Price Forecasting Based on Tree Regularization of GRU

Cited by 6 publications

References 17 publications

Deep Learning for Time Series Forecasting: A Survey

Deep Learning for Time Series Forecasting: A Survey

Stock Price Prediction Using a Frequency Decomposition Based GRU Transformer Neural Network

Enhanced Stock Market Forecasting: A Hybrid Multivariate Approach Incorporating CEEMDAN and Deep Recursive Neural Networks

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