Few-shot time series forecasting in a meta-learning framework

Abstract

Time series forecasting has a wide range of applications in various fields. To eliminate the need for time series data volume, a meta-learning-based few-shot time series forecasting method is proposed. This method uses a residual stack module as its backbone and connects the residuals forward and backward through a multilayer fully connected network so that the model and the meta-learning framework can be seamlessly combined. The Empirical knowledge of different time-sequence tasks is obtained through meta-training. To enable fast adaptation to new prediction tasks, a small meta-network is introduced to adaptively and dynamically generate the learning rate and weight decay coefficient of each step in the network. This method can use sequences of different data distribution characteristics for cross-task learning, and each training task only needs a small number of time series to achieve sequence prediction for the target task. The results show that compared with the two baselines, the proposed method has improved performance on 67.07% and 58.53% of the evaluated tasks. Thus, this method can effectively alleviate the problems caused by insufficient data during training and has broad application prospects in the field of time series.