Time-series analysis with smoothed Convolutional Neural Network

CNN originates from image processing and is not commonly known as a forecasting technique in time-series analysis which depends on the quality of input data. One of the methods to improve the quality is by smoothing the data. This study introduces a novel hybrid exponential smoothing using CNN called Smoothed-CNN (S-CNN). The method of combining tactics outperforms the majority of individual solutions in forecasting. The S-CNN was compared with the original CNN method and other forecasting methods such as Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM). The dataset is a year time-series of daily website visitors. Since there are no special rules for using the number of hidden layers, the Lucas number was used. The results show that S-CNN is better than MLP and LSTM, with the best MSE of 0.012147693 using 76 hidden layers at 80%:20% data composition.

Automated summary

This study introduces a novel hybrid approach called Smoothed-CNN (S-CNN) that combines exponential smoothing with CNN, outperforming other forecasting methods such as MLP and LSTM. The results show that S-CNN is better than MLP and LSTM, achieving the best MSE of 0.012147693 with 76 hidden layers at an 80%:20% data composition.