A Clustering and Neural Network Based Model for Multivariate Time-Series Prediction

• 1、Institute of network technology, Beijing University of Posts and Telecommunications, Beijing 100876

Abstract：Multivariate time series are widely distributed in our daily life. And multivariate time series prediction is extensively studied with ubiquitous applications across many domains. But ther are still challenges in it, like extracting short- and long-term dependency, capturing different periodic pattern. This paper proposes a novel deep learning framework named Clustering Convolutional Recurrent Neural Network. Starting with time series clustering, Convolutional Recurrent Neural Network and Flatten Convolutional Recurrent Neural Network extract differences and similarities among clusters and variables. This model also use attention mechanism and multiple convolution layers to capture different periodic patterns. This model has achieved significant improvement in diverse datasets and has great practicabilities, all experimental results will be shown through our paper later.

Keywords： Artificial intelligence Multivariate time series Attention Convolutional neural network Recurrent neural network