Deep Learning Framework for Physical Internet Hubs Inbound Containers Forecasting

Abstract

This article presents a framework for physical internet hubs inbound containers forecasting based on deep learning and time series analysis. The inbound containers forecasting is essential for planning, scheduling, and resources allocation. The proposed framework consists of three main phases. First, the inbound historical transaction has been processed to find out the training window size (lags) using auto correlation function (ACF) and partial autocorrelation function (PACF). Second, the framework uses convolutional neural network (CNN) and recurrent neural network (RNN) as training networks for the historical time series data in two techniques. The proposed framework uses univariate and multivariate time series analysis to explore the maximum forecasting outcomes. Last, the framework measures the accuracy and compares the forecasting output using mean absolute error matrix (MAE) for both approaches. The experiments illustrated that RNN forecasts univariate inbound transaction with total 5.0954 MAE rather than 5.0236 for CNN. The CNN outperforms multivariate inbound containers forecasting with 0.7978 MAE. All the results has been compared with autoregressive integrated moving average (ARIMA) and support vector machine (SVR).