Publication Type : Journal Article
Publisher : Journal of Computational and Theoretical Nanoscience, American Scientific Publishers
Source : Journal of Computational and Theoretical Nanoscience, American Scientific Publishers, Volume 17, Issue 1, p.260–266 (2020)
Url : https://www.ingentaconnect.com/contentone/asp/jctn/2020/00000017/00000001/art00041#Refs
Campus : Bengaluru
School : Department of Computer Science and Engineering, School of Engineering
Department : Computer Science
Year : 2020
Abstract : Forecasting a time series is an ever growing area in which various machine learning techniques have been used to predict and analyze the future based on the data gathered from past. “Prophet” forecasting model is the most recent development in forecasting the time series, developed by Facebook. Prophet is much faster and simpler to implement than the previous forecasting model such as ARIMA model. Classification of forecasting output can be done by applying convolution neural network (CNN) on the outcomes of the Prophet model. To get higher accuracy with lesser loss, the method runs CNN with the best possible deep layers. The yearly, weekly, daily seasonality and trends could be realized by Prophet Model. The paper shows classification of these output based on the varying types of seasonality and trends. The labeled output can then, train and test all the trends’ result and find out the accuracy and loss incurred in a CNN model. Applying different depth and parameters of CNN that is a combined unit at each layer, it can achieve more than 96% accuracy with less than 4% loss. The integration of prophet and CNN shows that the training and testing model of a neural network can validate the prediction done by using prophet forecasting model along with the seasonality and trends parameters are in coherence to one another.
Cite this Research Publication : A. Sahay and Amudha J., “Integration of Prophet Model and Convolution Neural Network on Wikipedia Trend Data”, Journal of Computational and Theoretical Nanoscience, vol. 17, no. 1, pp. 260–266, 2020.