An Artificial Intelligence Approach for Time Series Next Generation Applications

Abstract

With the emergence of the Internet of Things (IoT) applications, a huge amount of information is generated to help the optimization of operational cellular networks, smart transportation, and energy management systems. Applying Artificial Intelligence approaches to exploit this data seems to be promising. In this paper, we propose a dual deep neural network architecture. It is used to classify time series and to predict future data. It is essentially based on Long Short Term Memory (LSTM) algorithms for accurate time series prediction and on deep neural network, classifiers to classify input streams. It is shown to work on different domains (cellular, energy management, and transportation systems). Cloud architecture is used for IoT data collection and our algorithm is applied on real-time energy data for accurate energy classification and prediction.