IE5220: Assignment Time Series Prediction for weblink Traffic The 1st Due Date: March 13rd, 2020 Friday at 23:59 EST The 2nd Due Date: March 14rd, 2020 Saturday at 23:59 EST Background This assignment builds from our discussion of forecasting using different approaches to model the pattern of website daily views and automatically select the best model to predict future daily views for each website. Approaches are limited to three we discussed: 1. Time series clustering 2. Facebook Prophet 3. XGBoost Data File name: inputdata.csv The input dataset was collected with a daily view over a period between Jan 2017 and Jan 2020 (37 months) were measured. It consists of approximately 1,093 time series. Each of these time series represents a number of daily views of a different weblink, starting from January 1st, 2016 up until January 31st, 2020. The goal is to forecast the daily views into the future for each weblink in the dataset from February 1st, 2020 to December 31st, 2020. Requirements 1. Use Python 3.6 or 3.7 2. Turn in your code and figures along with summary based on your observation of model performance
