Air Quality Time Series Prediction Optimized by Grey Wolf Algorithm

Abstract

To enhance prediction reliability and accuracy, an Lstm model optimized by the improved grey wolf algorithm is introduced for daily air quality index forecasting. Firstly, the model preprocesses the collected data and divides the data into a training set and a testing set. Then, using Tent Chaotic Sequence to generate an initial population, which increases the diversity of individuals in the population; And aming at the shortage of the search ability of Grey Wolf Optimization (GWO), updating the parameters $a$. The improved GWO (IGWO) used to optimize the relevant hyperparameters in the long and short-term memory neural network. Finally, the IGWO-LSTM model constructed with excellent hyperparameters will use the test set to obtain the prediction results. The experimental results demonstrate the proposed method outperforms the other four model in AQI prediction.