Details

Autor(en) / Beteiligte

• Wang, Shiyu
• Sun, Yinbo
• Wang, Yan
• Zhou, Fan
• Ma, Lin-Tao
• Zhang, James
• Zheng, YangFei
• Shao, Yingxiao
• El Abbadi, Amr
• Yin, Hongzhi
• Dobbie, Gill
• Feng, Zhiyong
• Han, Wook-Shin
• Wang, Xin
• Sapino, Maria Luisa

Titel

Flow-Based End-to-End Model for Hierarchical Time Series Forecasting via Trainable Attentive-Reconciliation

Ist Teil von

• Database Systems for Advanced Applications, p.167-176

Ort / Verlag

Cham: Springer Nature Switzerland

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Time Series (TS) is one of the most common data formats in modern world, which often takes hierarchical structures, and is normally complicated with non-Gaussian and non-linear properties. Many businesses rely on accurate TS forecasting, under these complications, to help with operational efficiencies. In this paper, we present a novel approach for Hierarchical Time Series (HTS) prediction via trainable attentive reconciliation and Normalizing Flow (NF), which is used to approximate the complex (normally non-Gaussian) data distribution for multivariate TS forecasting. To reconcile the HTS data, we also propose a new flexible reconciliation strategy via the attention-based encoder-decoder neural network, unlike the existing methods with strong assumptions, such as unbiased forecasts and Gaussian noises. In addition, by using the reparameterization trick, we are able to combine forecasting and reconciliation into a trainable end-to-end model. Extensive empirical evaluations are conducted on real-world hierarchical datasets and the preliminary results demonstrate the efficacy of our proposed method.