Facebook recently released a API package allowing access to its forecasting model called prophet. According to the underling post:
It's not your traditional ARIMA-style time series model. It's closer in spirit to a Bayesian-influenced generalized additive model, a regression of smooth terms. The model is resistant to the effects of outliers, and supports data collected over an irregular time scale (ingliding presence of missing data) without the need for interpolation. The underlying calculation engine is Stan; the R and Python packages simply provide a convenient interface. After reading it, I got really curious about the predictive performance of this method for stock prices.