Comparison Study of Deep Learning and Time Series for Bioelectric Potential Analysis

Abstract

The study of bioelectric potential of plant has been conducted using some methods. Such as, decision tree (J48), multilayer perceptron, ANN, CNN, and etc. However, to find the best accuracy is a seriously challenge because the previous studies did not obtain a satisfied result. Furthermore, Because the data is sequence form, it is interesting if analyzed by deep learning (LSTM) and time series method (ARIMA). Both of methods are have the same characteristics. This approach is a new contribution for this topic. Therefore, the aim of this research is to compare LSTM and ARIMA method for analyzing bioelectric potential data set. For determining the accuracy, we use root mean square error (RMSE) and mean absolute error (MAE). Finally, in this case, the ARIMA model is better than LSTM method and presented a promise result.