Amazon Forecast

Discussion in 'Programming' started by ajensen, Nov 28, 2018.

  1. ajensen


    Amazon is starting a forecasting service for business, using methods employed in its own operations. Users will upload their time series data and use recipes to generate forecasts. The time series recipes Amazon offers (documentation is at the link) are

    Exponential Smoothing (ETS)
    Mixture Density Network (MDN)
    Multiquantile Recurrent Neural Network (MQRNN)
    Non-Parametric Time Series (NPTS)
    Spline Quantile Forecaster (SQF)

    In a sense, what traders are trying to do is forecast returns and position themselves accordingly. I am familiar with ARIMA and exponential smoothing, but some of the other models are new to me. Have people tried these approaches on financial time series? I do understand that asset returns are mostly random, but even a small amount of predictability can be exploited.
  2. Interesting. Thanks for sharing.
    I think what they are bringing to the table is simply more raw power and complexity.

    What's of significantly more value, is the knowledge of how to apply these tools successfully (or not).
    I don't really think they are selling the tools based on financial forecasts, but more on traditional time series applications (product demand, etc.).

    It would be useful to see some white papers. Other than general descriptions and cost of use, I didn't see much in the way of application notes, putting the burden on the user.
  3. 2rosy


  4. I did not see any courses specifically targeting financial machine learning and forecasting. I did see some courses on things like packaging optimization, which are similar to some previous Kaggle competitions.
    But not specifically geared towards Quantitative finance or trading.

    If you have seen any specific tables of results geared towards financial forecasting and trading, could you please paste any? Those would be very interesting.

    I do think a lot of the courses and material look interesting, however, thank you for sharing.