Time-Series Forecasting With Shape Attention*

Feihu Huang, Peiyu Yi, Jince Wang, Mengshi Li, Jian Peng

Abstract: The study of time series forecasting is significant and useful in a variety of scenarios. However, due to the high degree of randomness and the complex contextual factors, it remains a difficult challenge. While several works based on machine learning and deep neural network have been proposed in recent years to address these challenges, most of them mine sequence features based on discrete points and overlook the fact that shape similarity plays an important role in inferring the future values. In this paper, we propose a seq2seq model with Shape Attention and Dilated Convolution (SADC) to tackle this problem. SADC contains two important phases: (1) Embedding with multi-scale dilated convolution. We first define the shape as a set of discrete points in a fixed-length window. The features hidden in the shape are then learned using multiple dilated convolutions with different kernels. (2) Inferring with shape attention. During this phase, we first present the shape attention, which aims to provide support information for inferring future values by generating the embedding vector of each prediction window based on shape similarity. The PreNet network is then built to predict the values using the embedding vector for each prediction window. The experimental results conducted on two datasets show that the performance of SADC model outperforms the state-of-the-art models on time series forecasting.