809 research outputs found

    Research on the Construction of Sales Forecasting Model of Fashion Products Based on Feature Representation of Multimodal and Deep Learning

    Get PDF
    By improving the accuracy of sales forecasting, this paper provides support for fashion product sales enterprises to make better inventory management and operational decisions. The deep neural network is introduced into the construction of multimodal features, and the internal structure of different modes, such as historical sales features, picture features, and basic attribute features of products, are fully considered, and finally the sales forecasting model of fashion products based on multimodal feature fusion is constructed. In addition, combined with the actual data of the enterprise, the proposed model is compared with the exponential regression model and shallow neural network model. The paper finds that multimodal features and deep learning representation method has better performance than traditional methods (exponential regression and shallow neural network) in the task of predicting sales of fashion products. The results help enterprises use the deep learning method and the data of multiple modal to make accurate sales forecast

    Sequence modelling for e-commerce

    Get PDF

    Internet Financial Credit Risk Assessment with Sliding Window and Attention Mechanism LSTM Model

    Get PDF
    With the accelerated pace of market-oriented reform, Internet finance has gained a broad and healthy development environment. Existing studies lack consideration of time trends in financial risk, and treating all features equally may lead to inaccurate predictions. To address the above problems, we propose an LSTM model based on sliding window and attention mechanism. The model uses sliding windows to enable the model to effectively exploit the contextual relevance of loan data. And we introduce the attention mechanism into the model, which enables the model to focus on important information. The result on the Lending Club public desensitization dataset shows that our model outperforms ARIMA, SVM, ANN, LSTM, and GRU models

    Internet Financial Credit Risk Assessment with Sliding Window and Attention Mechanism LSTM Model

    Get PDF
    With the accelerated pace of market-oriented reform, Internet finance has gained a broad and healthy development environment. Existing studies lack consideration of time trends in financial risk, and treating all features equally may lead to inaccurate predictions. To address the above problems, we propose an LSTM model based on sliding window and attention mechanism. The model uses sliding windows to enable the model to effectively exploit the contextual relevance of loan data. And we introduce the attention mechanism into the model, which enables the model to focus on important information. The result on the Lending Club public desensitization dataset shows that our model outperforms ARIMA, SVM, ANN, LSTM, and GRU models

    Analysis and Forecast of Railway Freight Volume based on Prophet-Deep AR Model

    Get PDF
    The research on railway freight volume forecast is of great significance to the allocation of railway freight transport resources, the formulation of transport plans and the organization of railway freight transport. This study, by fully mining the railway freight ticket data information, put forward the precise forecast model of railway freight volume under different types of freight. Firstly, the railway freight ticket data are cleaned, regulated and integrated, and the time series of the daily number of railway freight trains are constructed, get the trend, periodicity and holiday data of railway traffic data, and set the parameters of Chinese holidays and rest days according to the demand characteristics of different categories. Secondly, the forecasting result of the Prophet is taken as a cooperative parameter. DeepAR is used to forecast, and a combined model of the Prophet-DeepAR is constructed. Finally, the combined model was validated with Shanghai Railway Bureau data from January 1, 2015 to December 31, 2018 for the food and tobacco category, and with Prophet-DeepAR, LSTM, Wavelet, BILSTM, and Prophet-LSTM, prophet-wavelet and Prophet-Bilstm are used to compare the prediction results. The results show that the Prophet-DeepAR model can extract the multi-dimensional periodicity of freight traffic and mine the trend information of freight traffic, and get the prediction result with high precision. It has better accuracy than other models

    A Novel Distributed Representation of News (DRNews) for Stock Market Predictions

    Full text link
    In this study, a novel Distributed Representation of News (DRNews) model is developed and applied in deep learning-based stock market predictions. With the merit of integrating contextual information and cross-documental knowledge, the DRNews model creates news vectors that describe both the semantic information and potential linkages among news events through an attributed news network. Two stock market prediction tasks, namely the short-term stock movement prediction and stock crises early warning, are implemented in the framework of the attention-based Long Short Term-Memory (LSTM) network. It is suggested that DRNews substantially enhances the results of both tasks comparing with five baselines of news embedding models. Further, the attention mechanism suggests that short-term stock trend and stock market crises both receive influences from daily news with the former demonstrates more critical responses on the information related to the stock market {\em per se}, whilst the latter draws more concerns on the banking sector and economic policies.Comment: 25 page
    • ā€¦
    corecore