Prediction of Index Trend based on LSTM Model for Extracting Image Similarity Feature

Guo, Jin; Li, Xuwei

doi:10.1145/3349341.3349427

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…Another active and related field to trend prediction is the stock market direction movement, which is only concerns with the direction of the time series, it does not predict the strength and the duration of the time series [5,23,6,16,19,9]. Generally, the baseline methods used by prior works include neural networks, the naive last value prediction, ARIMA, SVR [15,24].…”

Section: Related Workmentioning

confidence: 99%

An Analysis of Deep Neural Networks for Predicting Trends in Time Series Data

Kouassi

Moodley

2020

Artificial Intelligence Research

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Recently, a hybrid Deep Neural Network (DNN) algorithm, TreNet was proposed for predicting trends in time series data. While TreNet was shown to have superior performance for trend prediction to other DNN and traditional ML approaches, the validation method used did not take into account the sequential nature of time series data sets and did not deal with model update. In this research we replicated the TreNet experiments on the same data sets using a walk-forward validation method and tested our optimal model over multiple independent runs to evaluate model stability. We compared the performance of the hybrid TreNet algorithm, on four data sets to vanilla DNN algorithms that take in point data, and also to traditional ML algorithms. We found that in general TreNet still performs better than the vanilla DNN models, but not on all data sets as reported in the original TreNet study. This study highlights the importance of using an appropriate validation method and evaluating model stability for evaluating and developing machine learning models for trend prediction in time series data.

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Section: Related Workmentioning

confidence: 99%

An Analysis of Deep Neural Networks for Predicting Trends in Time Series Data

Kouassi

Moodley

2020

Artificial Intelligence Research

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“…Different from [6], in our three-dimensional input tensor, the technical indicators are transformed into deterministic trend signals and the stock indices are ranked by PPMCC. Actually, PPMCC is one of the most common measurements of determining linear dependence, which is capable of reflecting the degree of linear correlation between two variables [33,34]. e calculation formula is as , follows:…”

Section: Input Tensor Buildingmentioning

confidence: 99%

Deep Learning for Price Movement Prediction Using Convolutional Neural Network and Long Short-Term Memory

Yang

Zhai

Tao

2020

Mathematical Problems in Engineering

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The prediction of stock price movement direction is significant in financial studies. In recent years, a number of deep learning models have gradually been applied for stock predictions. This paper presents a deep learning framework to predict price movement direction based on historical information in financial time series. The framework combines a convolutional neural network (CNN) for feature extraction and a long short-term memory (LSTM) network for prediction. We specifically use a three-dimensional CNN for data input in the framework, including the information on time series, technical indicators, and the correlation between stock indices. And in the three-dimensional input tensor, the technical indicators are converted into deterministic trend signals and the stock indices are ranked by Pearson product-moment correlation coefficient (PPMCC). When training, a fully connected network is used to drive the CNN to learn a feature vector, which acts as the input of concatenated LSTM. After both the CNN and the LSTM are trained well, they are finally used for prediction in the testing set. The experimental results demonstrate that the framework outperforms state-of-the-art models in predicting stock price movement direction.

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Portfolio Construction with Stock Prices Predicted by LSTM using Enhanced Features

Ozbilen

Yaslan

2021

2021 6th International Conference on Computer Science and Engineering (UBMK)

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Prediction of Index Trend based on LSTM Model for Extracting Image Similarity Feature

Cited by 3 publications

References 4 publications

An Analysis of Deep Neural Networks for Predicting Trends in Time Series Data

An Analysis of Deep Neural Networks for Predicting Trends in Time Series Data

Deep Learning for Price Movement Prediction Using Convolutional Neural Network and Long Short-Term Memory

Portfolio Construction with Stock Prices Predicted by LSTM using Enhanced Features

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