Sales prediction is crucial for business intelligence, aiding in workforce management or resource allocation. Accurate sales forecasting is vital for financial planning and predicting both short-term and long-term company performance. In this work, we pr
Our approach is based on a Mixture of Experts (MoE) model using LSTM networks, with block cross-validation. We compare our proposal to various standard models in prediction tasks. Experiments show that our model achieves greater generalization on unseen stores compared to other models. As future ...
List of papers, code and experiments using deep learning for time series forecasting deep-neural-networksdeep-learningtime-seriestensorflowpredictionpython3pytorchrecurrent-neural-networkslstmseries-analysisforecasting-modelslstm-neural-networksdemand-forecastingseries-forecastingsales-forecastingtime-series-classificati...
In the code below, we define our model and then make dynamic predictions for the last 12 months of the data. For standard, non-dynamic predictions, the following month’s prediction is made using the actual sales from the prior months. In contrast, for dynamic predictions, the following mont...
The prediction error is adjusted through an LSTM based deep learning model. Wang et al. [12] used different historical sales ranking and other data of books from different online stores to build a model for predicting book sales. The data is preprocessed, feature selection is carried out, and...
, Ogihara, Ren, and Lee (2019), for instance, process the raw content data by the CNN parts, then the CNN features (outputs from the CNN layers) are fed into the LSTM (long-term short-term) layers, and the fully connected CNN layers fuse together the data and generate the prediction...
Our approach is based on a Mixture of Experts (MoE) model using LSTM networks, with block cross-validation. We compare our proposal to various standard models in prediction tasks. Experiments show that our model achieves greater generalization on unseen stores compared to other models. As future ...
Also, Ridge and LinearRegression had the best results for stock price prediction. Based on results, the best indicators to predict stock price are: the ... F Moodi,A Jahangard-Rafsanjani,S Zarifzadeh 被引量: 0发表: 2023年 Corporate social responsibility disclosure prediction using LSTM neural...
Sales prediction can help companies to manage resources more effectively, such as cash flow and production, and create better business plans. Sales forecasting allows the company to be proactive instead of reactive. It can help a company in adopting a suitable production policy. It also helps in...
In this study, prediction of product sales as they relate to changes in temperature is proposed. This model uses long short-term memory (LSTM), which has shown excellent performance for time series predictions. For verification of the proposed sales prediction model, the sales of short pants, ...