This chapter shows the ways to implement time series forecasting using machine learning methods, which includes regression tree, random forest, gradient boost machine, elastic net, and automatic machine learning procedure (autoML), etc. In terms of visual comparison, we may be justified in saying that, through cost-benefit evaluation, the time-consuming characteristic of machine learning may cause it to underperform conventional ARIMA.