Abstract
The use of deep learning methods to solve problems in the field of artwork prices has attracted widespread attention, especially the superiority of long short-term memory network (LSTM) in dealing with time series problems. However, the potential for deep learning in the prediction of artwork price has not been fully explored. This paper proposes a deep prediction network structure that considers the correlation between time series data and the combination of two-way LSTM as well as one-way LSTM networks to predict the price of artworks. This paper proposes a deep-level two-way and one-way LSTM to predict the price of artworks in the art market. Taking into account the potential reverse dependence of the time series, the bidirectional LSTM layer is used to obtain bidirectional time correlation from historical data. This research uses a matrix to represent the artwork price data and fully considers the spatial correlation characteristics of the artwork price. Simultaneously, this paper uses the two-way LSTM network to correlate the potential contextual information of the historical data of the artwork price stream and fully perform feature learning. This study applies the two-way LSTM network layer to the building blocks of the deep architecture to measure the inverse dependence of the price fluctuation data. The comparison with other prediction models shows that the LSTM neural network fused with one-way and two-way proposed in this paper is superior to other neural networks for predicting price of artworks in terms of prediction accuracy.
Highlights
The traditional analysis model requires that the price time series itself be stable; that is, the mean and variance of the data itself cannot change with time [9]
Evaluation on the Number of Network Layers. e main neural network frameworks used in this experiment are long short-term memory network (LSTM) and two-way LSTM (TWLSTM) networks. erefore, the performance of the entire network is closely related to the number of LSTM and TWLSTM layers taken. is section mainly discusses the selection of LSTM and TWLSTM network layers in the experiment
It can be clearly seen that when the number of TWLSTM network layers is two, the best performance can be obtained
Summary
Transactions are conducted in different markets to find the optimal allocation of assets, which makes it more difficult to capture the price of an asset It is precisely because of price uncertainty that a reasonable quantitative estimate of price has become the basis for more and more risk management strategies [4]. Traditional analysis models have certain prerequisites for the characteristics of price time series [7, 8]. Is is difficult to effectively characterize the volatility characteristics and often requires more price factors to model, so as to enhance the interpretation [10]. E deep neural network can improve the traditional price time series analysis model and effectively enhance the interpretation [11–13]. In the field of art price fluctuation prediction, we can use the learning ability of deep learning models to effectively extract the features of price time series, so as to achieve the purpose of feature extraction and prediction [19, 20]. Based on the deep neural network, this work designs a new method for predicting the price fluctuation of artworks
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