A dual-head attention model for time series data imputation
Yifan Zhang, Peter J. Thorburn
Abstract
Digital agriculture increasingly relies on the availability and accuracy of measurement data collected from various sensors. Of this data, water quality attracts great attention due to its intended use for crop irrigation, livestock, and other farming activities. Accurate and reliable water quality measurements enable farmers to understand the landscape comprehensively, optimising resource utilisation and reducing the negative impacts of agriculture on the environment. In practice, missing and incomplete data can create biased estimations and reduce the efficiency of many of the valuable applications provided by digital agriculture. The purpose of this paper is to propose a dual-head sequence-to-sequence imputation model (Dual-SSIM) designed to impute missing time series data in sensor networks, therefore reducing the negative consequences of missing and incomplete data. Unlike standard sequence-to-sequence architecture, the Dual-SSIM model features two encoders with gated recurrent units (GRUs) which are used to process temporal information before and after the missing gap separately. Furthermore, the attention mechanism is applied to two encoder outputs concurrently, in order to allow the model to focus on the high relative inputs when estimating missing data. The performance efficacy of Dual-SSIM has been investigated through the monitoring of water quality, sourced from an Australian water quality information system. Experimental results of this investigation indicate that Dual-SSIM outperforms associated alternatives based on the mean absolute error (MAE), root mean square error (RMSE), and dynamic time warping (DTW) scores in imputing two different water quality variables. Therefore, it can be concluded that Dual-SSIM provides an effective and promising approach for water quality data imputation.