Wavelet gated multiformer for groundwater time series forecasting.

ABSTRACT: Developing accurate models for groundwater control is paramount for planning and managing life-sustaining resources (water) from aquifer reservoirs. Significant progress has been made toward designing and employing deep-forecasting models to tackle the challenge of multivariate time-series forecasting. However, most models were initially taught only to optimize natural language processing and computer vision tasks. We propose the Wavelet Gated Multiformer, which combines the strength of a vanilla Transformer with the Wavelet Crossformer that employs inner wavelet cross-correlation blocks. The self-attention mechanism (Transformer) computes the relationship between inner time-series points, while the cross-correlation finds trending periodicity patterns. The multi-headed encoder is channele

SUBMITTER: Serravalle Reis Rodrigues VH 

PROVIDER: S-EPMC10404297 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature