Selcuk Disci contrasts a couple of methods for time series forecasting:
It is always hard to find a proper model to forecast time series data. One of the reasons is that models that use time-series data often expose to serial correlation. In this article, we will compare k nearest neighbor (KNN) regression which is a supervised machine learning method, with a more classical and stochastic process, autoregressive integrated moving average (ARIMA).
We will use the monthly prices of refined gold futures(XAUTRY) for one gram in Turkish Lira traded on BIST(Istanbul Stock Exchange) for forecasting. We created the data frame starting from 2013. You can download the relevant excel file from here.
Click through for the demonstration. H/T R-Bloggers.