LR data collected by the image monitoring system were pre-processed by interpolation and data normalization before feeding into the TCN forecasting model. Sample results are shown in Figure 1. Interpolation is a mathematical method that fits a function to a dataset and uses the function to fill in missing data based on the nearest past and future values. Each set of missing data was filled in with the mean computed from its four nearest values. Afterwards, the completed data were normalized by scaling the input feature into values from 0 to 1 based on the minimum and maximum values of each input feature, respectively. Dataset EA had five days of missing data while dataset EB had eight days of missing data, both due to power outage and maintenance of the image monitoring system.

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