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We are interested in forecasting a large collection of FMCG demand time series. As the demand of FMCG exists in a hierarchy (from manufacturers to distributors to retailers), the bottom level of the hierarchy may contain thousands or even millions of time series. Producing aggregate consistent forecasts while utilizing the unique features from each time series thus become a technical challenge. To...
By the analyze of chaos for runoff series, combing the reconstruction phase space theory and BP neural network to develop the BP neural network model based reconstruction phase space, and forecast the runoff series mensal in Xiaoqing river hydrological station of Jinan, the result shows that the model has a very good forecast accuracy and value.
By the main component analysis, and maximum Lyapunov index method, this paper analyses chaotic character of ground water level time series. On this basis, combining the reconstruction phase space of chaos theory with BP neural network to set up a BP neural network model based on chaos theory. This paper forecasts ground water level of the Heihu Spring in Jinan by the model. The result shows that the...
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