[djhworld]

I'm currently learning machine learning at the most basic level, this is the sort of stuff I want to work towards though

I deal with time series data a lot at work, I work in broadcasting/media and 99% of the time the data is fairly "predictable" and follows a regular daily pattern, peppered with the odd spikes during big, unpredicatble news events.

[dafrie]

A year ago, the original blog post [1] (it was just recently updated, which is now the one linked here on HN) helped me on a semester thesis, where I quite successfully used LSTM for short-term electricity load forecasting, which also has very strong daily, weekly and seasonal patterns. I used multiple features/variables such as calendar and weather data and found the LSTM models to easily beat ARIMA/TBATS forecasts.

You can find the code repo on my Github link [2], but please bear with the code quality. I only have an economics background, so my coding experience is fairly limited :)

[1] http://www.jakob-aungiers.com/articles/a/LSTM-Neural-Network...

[2] https://github.com/dafrie/lstm-load-forecasting

[md2be]

Time series analysis requires the data to be stationary.

[dafrie]

Well, I don't want to be pedantic, but don't you rather mean "Most TSA MODELS require data to be stationary"? My experience has been, that often practical TSA actually involves how to deal (testing, differencing, smoothing...) with non-stationarity, which is often not a trivial task...