Time series forecasting is a significant problem in many applications, e.g., financial predictions and business optimization. Modern datasets can have multiple correlated time series, which are often generated with global (shared) regularities and local (specific) dynamics. In this paper, we seek to tackle such forecasting problems with DeepDGL, a deep forecasting model that disentangles dynamics into global and local temporal patterns. DeepDGL employs an encoder-decoder architecture, consisting of two encoders to learn global and local temporal patterns, respectively, and a decoder to make multi-step forecasting. Specifically, to model complicated global patterns, the vector quantization (VQ) module is introduced, allowing the global feature encoder to learn a shared codebook among all time series. To model diversified and heterogenous local patterns, an adaptive parameter generation module enhanced by the contrastive multi-horizon coding (CMC) is proposed to generate the parameters of the local feature encoder for each individual time series, which maximizes the mutual information between the series-specific context variable and the long/short-term representations of the corresponding time series. Our experiments on several real-world datasets show that DeepDGL outperforms existing state-of-the-art models.
翻译:在许多应用中,例如金融预测和商业优化中,时间序列预测是一个重大问题。现代数据集可能具有多个相关时间序列,通常由全球(共享)规律和本地(特定)动态生成。在本文件中,我们寻求与DeepDGL(一个将动态分解成全球和地方时间模式的深层预测模型)一起解决此类预测问题。DiepDGL(DiggL)使用一个编码器解码器结构,由两个编码器组成,分别学习全球和地方时间模式,以及一个解码器组成,进行多步预测。具体而言,为模拟复杂的全球模式,引入矢量定量(VQ)模块,让全球特性编码器在所有时间序列中学习共享的代码。为了模拟多样化和异质性本地模式,建议采用一个适应性参数生成模块,通过对比性多色谱编码(CMC),为每个单个时间序列生成本地特征编码器的参数,从而尽可能扩大一系列具体环境变量与长期/短期时间序列之间的相互信息。我们关于相应时程模型的现有数据结构的实验将显示现状。