DEEP LEARNING FOR SIMULTANEOUS IMPUTATION AND CLASSIFICATION OF TIME SERIES INCOMPLETE DATA

Abstract

Classification with time series data has many practical applications in finance, medicine, manufacturing, energy, transportation, and agriculture. However, time series data is often plagued by missing values, which not only decreases the accuracy but also increases the complexity of classification models. A common approach to address classification with missing data is to use imputation methods to estimate the missing values before constructing classification models. Recurrent neural network models have been shown to be effective in estimating missing values for time series data. On the other hand, convolutional neural network models have demonstrated their effectiveness in building classification models for time series data. However, there has been no research that specifically addresses how to combine these two types of models to simultaneously address the problem of time series classification with incomplete data. Therefore, in this paper, a model combining recurrent neural networks and convolutional neural networks is proposed to build a model that can simultaneously estimate missing values and classify time series data. The experimental results demonstrate that the proposed model performs better than the existing methods for time series classification with incomplete data.