Stock Trend Prediction: A Semantic Segmentation Approach

• URL: http://arxiv.org/abs/2303.09323v1
• Date: Thu, 9 Mar 2023 01:29:09 GMT
• Title: Stock Trend Prediction: A Semantic Segmentation Approach
• Authors: Shima Nabiee, Nader Bagherzadeh
• Abstract summary: We present a novel approach to predict long-term daily stock price change trends with fully 2D-convolutional encoder-decoders. Our hierarchical structure of CNNs makes it capable of capturing both long and short-term temporal relationships effectively.
• Score: 3.718476964451589
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Market financial forecasting is a trending area in deep learning. Deep learning models are capable of tackling the classic challenges in stock market data, such as its extremely complicated dynamics as well as long-term temporal correlation. To capture the temporal relationship among these time series, recurrent neural networks are employed. However, it is difficult for recurrent models to learn to keep track of long-term information. Convolutional Neural Networks have been utilized to better capture the dynamics and extract features for both short- and long-term forecasting. However, semantic segmentation and its well-designed fully convolutional networks have never been studied for time-series dense classification. We present a novel approach to predict long-term daily stock price change trends with fully 2D-convolutional encoder-decoders. We generate input frames with daily prices for a time-frame of T days. The aim is to predict future trends by pixel-wise classification of the current price frame. We propose a hierarchical CNN structure to encode multiple price frames to multiscale latent representation in parallel using Atrous Spatial Pyramid Pooling blocks and take that temporal coarse feature stacks into account in the decoding stages. Our hierarchical structure of CNNs makes it capable of capturing both long and short-term temporal relationships effectively. The effect of increasing the input time horizon via incrementing parallel encoders has been studied with interesting and substantial changes in the output segmentation masks. We achieve overall accuracy and AUC of %78.18 and 0.88 for joint trend prediction over the next 20 days, surpassing other semantic segmentation approaches. We compared our proposed model with several deep models specifically designed for technical analysis and found that for different output horizons, our proposed models outperformed other models.

Related papers

• PDETime: Rethinking Long-Term Multivariate Time Series Forecasting from the perspective of partial differential equations [49.80959046861793]
  We present PDETime, a novel LMTF model inspired by the principles of Neural PDE solvers. Our experimentation across seven diversetemporal real-world LMTF datasets reveals that PDETime adapts effectively to the intrinsic nature of the data.
  arXiv  Detail & Related papers  (2024-02-25T17:39:44Z)
• Explainable Parallel RCNN with Novel Feature Representation for Time Series Forecasting [0.0]
  Time series forecasting is a fundamental challenge in data science. We develop a parallel deep learning framework composed of RNN and CNN. Extensive experiments on three datasets reveal the effectiveness of our method.
  arXiv  Detail & Related papers  (2023-05-08T17:20:13Z)
• Financial Time Series Forecasting using CNN and Transformer [16.57996422431636]
  We propose to harness the power of CNNs and Transformers to model both short-term and long-term dependencies within a time series. In our experiments, we demonstrated the success of the proposed method in comparison to commonly adopted statistical and deep learning methods.
  arXiv  Detail & Related papers  (2023-04-11T00:56:57Z)
• Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement Prediction [20.879245331384794]
  Recent deep neural network (DNN) methods capture multi-order dynamics using hypergraphs, but rely on the Fourier basis in the convolution. We propose a framework for stock movement prediction to overcome the above issues. Our framework outperforms state-of-the-art methods in terms of profit and stability.
  arXiv  Detail & Related papers  (2022-11-11T01:58:18Z)
• FuTH-Net: Fusing Temporal Relations and Holistic Features for Aerial Video Classification [49.06447472006251]
  We propose a novel deep neural network, termed FuTH-Net, to model not only holistic features, but also temporal relations for aerial video classification. Our model is evaluated on two aerial video classification datasets, ERA and Drone-Action, and achieves the state-of-the-art results.
  arXiv  Detail & Related papers  (2022-09-22T21:15:58Z)
• Improved Predictive Deep Temporal Neural Networks with Trend Filtering [22.352437268596674]
  We propose a new prediction framework based on deep neural networks and a trend filtering. We reveal that the predictive performance of deep temporal neural networks improves when the training data is temporally processed by a trend filtering.
  arXiv  Detail & Related papers  (2020-10-16T08:29:36Z)
• Liquid Time-constant Networks [117.57116214802504]
  We introduce a new class of time-continuous recurrent neural network models. Instead of declaring a learning system's dynamics by implicit nonlinearities, we construct networks of linear first-order dynamical systems. These neural networks exhibit stable and bounded behavior, yield superior expressivity within the family of neural ordinary differential equations.
  arXiv  Detail & Related papers  (2020-06-08T09:53:35Z)
• Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks [91.65637773358347]
  We propose a general graph neural network framework designed specifically for multivariate time series data. Our approach automatically extracts the uni-directed relations among variables through a graph learning module. Our proposed model outperforms the state-of-the-art baseline methods on 3 of 4 benchmark datasets.
  arXiv  Detail & Related papers  (2020-05-24T04:02:18Z)
• Forecasting Sequential Data using Consistent Koopman Autoencoders [52.209416711500005]
  A new class of physics-based methods related to Koopman theory has been introduced, offering an alternative for processing nonlinear dynamical systems. We propose a novel Consistent Koopman Autoencoder model which, unlike the majority of existing work, leverages the forward and backward dynamics. Key to our approach is a new analysis which explores the interplay between consistent dynamics and their associated Koopman operators.
  arXiv  Detail & Related papers  (2020-03-04T18:24:30Z)
• Convolutional Tensor-Train LSTM for Spatio-temporal Learning [116.24172387469994]
  We propose a higher-order LSTM model that can efficiently learn long-term correlations in the video sequence. This is accomplished through a novel tensor train module that performs prediction by combining convolutional features across time. Our results achieve state-of-the-art performance-art in a wide range of applications and datasets.
  arXiv  Detail & Related papers  (2020-02-21T05:00:01Z)
• Conditional Mutual information-based Contrastive Loss for Financial Time Series Forecasting [12.0855096102517]
  We present a representation learning framework for financial time series forecasting. In this paper, we propose to first learn compact representations from time series data, then use the learned representations to train a simpler model for predicting time series movements.
  arXiv  Detail & Related papers  (2020-02-18T15:24:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.