MAML-Enhanced LSTM for Air Quality Time Series Forecasting

Abstract

Predicting air quality is essential for environmental monitoring and public health. In this work, we suggest a novel method for time series forecasting that uses Long Short-Term Memory (LSTM) networks and the Model-Agnostic Meta-Learning (MAML) algorithm to explicitly target air quality factors. The dataset employed includes features such as carbon monoxide concentration, sensor responses, and meteorological variables. Through extensive experimentation, our MAML-enhanced LSTM model demonstrates improved adaptability to new air quality forecasting tasks, particularly when data is limited. We present comprehensive results, including comparisons with traditional LSTM models, highlighting the efficacy of the proposed approach. This research contributes to the advancement of meta-learning techniques in the domain of environmental monitoring and offers insights into the potential of MAML for enhancing time series forecasting models.