Multi-level lag scheme significantly improves training efficiency in deep learning: a case study in air quality alert service over sub-tropical area - Benedito Chi Man Tam; Su-Kit Tang; Alberto Cardoso (Journal of Big Data)
Abstract In environmental monitoring, deep learning models are used where we can either use past observations or extrapolated values with high uncertainty as input. The lag scheme is commonly applied during the modeling and construction process, in the application of deep learning models in multivariate time series prediction. For an adaptive feature engineering, an automated lag scheme is essential for improving the training efficiency. In multivariate time series (MTS) models, the predictive accuracy of artificial neural network ANN-type models can be improved by including more features. It is assumed that when processing a certain number of multivariate features, the timeliness and lag time of the inter-influencing between any pair of elements are different. This research aims to adopt an adaptive approach to solve it, namely, multi-level lag scheme. The research methods include literature review, searching for relevant technology frontiers, feasibility studies, selection and design solutions, modeling, data collection and pre-processing, experiments, evaluation, comprehensive analysis and conclusions. In proof of concept, we demonstrated a practical case of seasonal ANN type MTS model and public service on air quality. In terms of models, ANN type models were attempted with ARIMA as the comparing baseline. We used public data set of more than two base stations with pollution varying from low to high and including southern to northern district of a small city. Conclusions can be drawn from the analysis of multiple experimental results, proving that the proposed solution can effectively improve the training efficiency of the model. This is of great significance, so that most such models can be implemented to adaptively use lagged past measured data as input, instead of synchronously inputting future prediction values, which can greatly improve the practical application of the model in predictive ability.