A Time Series Data Filling Method Based on LSTM-Taking the Stem Moisture as an Example

Wei Song; Chao Gao; Yue Zhao; Yandong Zhao

doi:10.3390/s20185045

A Time Series Data Filling Method Based on LSTM-Taking the Stem Moisture as an Example

Sensors (Basel). 2020 Sep 5;20(18):5045. doi: 10.3390/s20185045.

Authors

Wei Song^{1

2}, Chao Gao³, Yue Zhao^{1

2}, Yandong Zhao^{1

2}

Affiliations

¹ School of Technology, Beijing Forestry University, Beijing 100083, China.
² Beijing Laboratory of Urban and Rural Ecological Environment, Beijing Forestry University, Beijing 100083, China.
³ School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China.

Abstract

In order to solve the problem of data loss in sensor data collection, this paper took the stem moisture data of plants as the object, and compared the filling value of missing data in the same data segment with different data filling methods to verify the validity and accuracy of the stem water filling data of the LSTM (Long Short-Term Memory) model. This paper compared the accuracy of missing stem water data for plants under different data filling methods to solve the problem of data loss in sensor data collection. Original stem moisture data was selected from Lagerstroemia Indica which was planted in the Haidian District of Beijing in June 2017. Part of the data which treated as missing data was manually deleted. Interpolation methods, time series statistical methods, the RNN (Recurrent Neural Network), and LSTM neural network were used to fill in the missing part and the filling results were compared with the original data. The result shows that the LSTM has more accurate performance than the RNN. The error values of the bidirectional LSTM model are the smallest among several models. The error values of the bidirectional LSTM are much lower than other methods. The MAPE (mean absolute percent error) of the bidirectional LSTM model is 1.813%. After increasing the length of the training data, the results further proved the effectiveness of the model. Further, in order to solve the problem of one-dimensional filling error accumulation, the LSTM model is used to conduct the multi-dimensional filling experiment with environmental data. After comparing the filling results of different environmental parameters, three environmental parameters of air humidity, photosynthetic active radiation, and soil temperature were selected as input. The results show that the multi-dimensional filling can greatly extend the sequence length while maintaining the accuracy, and make up for the defect that the one-dimensional filling accumulates errors with the increase of the sequence. The minimum MAPE of multidimensional filling is 1.499%. In conclusion, the data filling method based on LSTM neural network has a great advantage in filling the long-lost time series data which would provide a new idea for data filling.

Keywords: LSTM neural network; data filling; missing data; stem moisture.

MeSH terms

Memory, Long-Term*
Neural Networks, Computer*
Temperature