A deep learning framework with convolutional long short‐term memory for influenza‐like illness trend estimation

Abstract

AbstractForecasting and modeling real‐world time series problems have been challenging for researchers due to nonlinear and complicated patterns. With the recent developments in deep learning, more accurate and efficient forecasting can be provided by learning the relationships between input and output data. This article has introduced a novel hybrid approach, called the GA‐ConvLSTM‐CNN, based on the genetic algorithm (GA), convolutional long short‐term memory (ConvLSTM) and convolutional neural network (CNN) to capture complex and sophisticated features from historical data. The GA‐ConvLSTM‐CNN approach is constructed with two‐stacked ConvLSTM networks, two‐stacked CNN, and a fully connected layer to alleviate the complexity of the real‐world time series problems with multivariate and multistep. In order to increase the accuracy of the GA‐ConvLSTM‐CNN, the genetic algorithm is adopted to simultaneously optimize the network's hyperparameters. With the aim of making comparisons, the weekly influenza‐like illness (ILI) and the weather data have been handled. The GA‐ConvLSTM‐CNN framework compared different techniques comprising machine learning and deep learning approaches. The results have indicated that the GA‐ConvLSTM‐CNN has the ability to accomplish more effective prediction accuracy and provide remarkable support for ILI outbreaks than the benchmark techniques.