SALES forecastingFORECASTINGTIME series analysisPREDICTION modelsDEEP learningIn 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...
A Daily Sales Forecasting Method Based on LSTM Model WU Juan -juan ,REN Shuai ,ZHANG Wei -gang ,WU Jing ,LI Xiang -yun (School of Information Engineering ,Chang ’an University ,Xi ’an 710064,China )Abstract :Accurate sales forecasting has great guiding significance for business operations....
基于xgboost和lstm加权组合模型在销售预测的应用
Predicting stock prices using a TensorFlow LSTM (long short-term memory) neural network for times series forecasting financemachine-learningdeep-neural-networkscryptodeep-learningtime-seriesjupyter-notebookstockrecurrent-neural-networkscryptocurrencylstmlstm-modelmarket-datastock-priceslstm-neural-networksstock-pr...
A machine learning-based analytical intelligence system for forecasting demand of new products based on chlorophyll: a hybrid approach (LSTM) recurrent deep neural network, and a support vector machine (SVM) were trained to select the best model for each product based on a forecast ... R Rodrig...
history = model.fit(x_train, y_train, epochs=200, batch_size=20, validation_split=0.15, shuffle=True) # 模型预测 y_pred = model.predict(x_test) # 还原数据的缩放 y_pred = Robust_scale.inverse_transform(y_pred) #计算模型性能
Abstract:In the field of petrochemical industry, the prediction of gasoline sales affects managers' important decisions on sales planning and resource scheduling. In order to get better performance on prediction, we constructed SARIMA model based on statistics and LSTM model based on machine learning,...
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 propose the use of adaptive ensembles of classificati...
Karevan Z, Suykens JAK (2020) Transductive LSTM for time-series prediction: an application to weather forecasting. Neural Netw 125:1–9 Google Scholar Ding G, Qin L (2019) Study on the prediction of stock price based on the associated network model of LSTM. Int J Mach Learn & Cyber 11...
propertiesoftimeseriesdata.BasedonthethreeevaluationindicatorsofMSE,RMSE, 2 andR,thepredictioneffectofG-SARIMA-BP-LSTMmodelandSARIMA, BP-LSTMandSARIMA-BP-LSTMwascomparedandanalyzed.Inthethirdchapter, monthlysalesdataofnewenergyvehiclefromJanuary2017toDecember2022are ...