During a technical meeting a few weeks ago, we had a discussion about feature interactions, and how far we have to go with them so that we can capture possible relationships with our targets. Should we create (and select) arithmetic interactions between our features? A few years ago I remember visiting a website that showed how different models approximated these simple operations. It went from linear models to a complex Random Forest....
You got a lot of time series data points and want to predict the next step (or steps). What should you do now? Train a model for each series? Is there a way to fit a model for all the series together? Which is better? I have seen many data scientists think about approaching this problem by creating a single model for each product. Although this is one of the possible solutions, it’s not likely to be the best....
Candlestick chart patterns are one of the most widely known techniques that claim to “predict” the market direction inside technical analysis circles. The development of this technique goes back to 18th century Japan, and it’s attributed to a Japanese rice trader. It consists of finding patterns based on charts made of the above figure with prices over a period of time. There are many patterns, old and new, available on the internet....
Time series are wonderful. I love them. They are everywhere. And we even get to brag about being able to predict the future! As a follow-up to the article on predicting multiple time-series, I receive lots of messages asking about prediction for more than a single step. A step can be any period of time: a day, a week, a minute, an year… So this is called multi-step forecasting. I want to show you how to do it with neural networks....