Bing Wang

Time series forecasting has been an important tool in many areas such as agriculture, finance, management, production or sales. In recent years, a large literature has evolved on the use of artificial neural networks (ANN) in time series forecasting. However conventional ANN is limited by its instantaneous synchronization inputs, it is difficult to express accumulative time effect and lacks certain processing ability for uncertainty factors (e.g., randomness, fuzziness) hidden in time series. Thus a cloud process neural network (CPNN) model is put forward in the paper for time series forecasting. It combines cloud model's expression ability for uncertainty concepts and process neural network's dynamic signal processing method, converts quantitative time series inputs into multiple qualitative sub-cloud concepts, and then finds out the association rule between input and output variables through mining inherent law among multiple sub-clouds. For CPNN learning, this paper proposes a learning strategy based on cat swarm optimization algorithm, which could optimize the network structure and learning parameters simultaneously to improve the network approximation and generalization ability. Finally, the model and algorithm is used in individual household electric power consumption time series forecasting and ASP flooding oil recovery index forecasting. In order to improve the quality of training samples, phase space reconstruction theory is employed to reconstruct one-dimensional time series into high-dimensional phase space as training sample set.