Random Forests for Time Series Data

Music City Center 

Time series data sets in economics and finance often exhibit nonlinearities that are not captured well by traditional autoregressive integrated moving average (ARIMA) models alone. Random forests have gained popularity in these types of forecasting tasks largely because of their ability to capture nonlinearity and feature interactions. However, in its current form, random forests do not leverage autocorrelation present in time series data. In this talk, we will discuss some hybrid strategies to combine the strengths of random forests with those of traditional ARIMA models and demonstrate their effectiveness on high-frequency trading data sets.