A machine learning methodology, called flow field forecasting, is proposed for statistically predicting the future of a univariate time series. Flow field forecasting draws information from the interpolated flow field of an observed time series to build a forecast step-by-step. Flow field forecasting is presented with examples, a discussion of its properties relative to other common forecasting techniques, and a statistical error analysis.