Former Reuters Research Director here. When modeling time series, you will want a model that is sensitive both to short term and longer term movements. In other words, a Long Term Short Term Memory (LSTM). Sepp Hochreiter invented this concept in his Master's thesis supervised by Jürgen Schmidhuber in Munich in the 1990s; today, it's the most-cited type of neural network. Here are papers describing it: https://people…
I don't think LSTMs are state-of-the-art in the domain anymore.
Ask HN: Data Scientists, what libraries do you use for timeseries forecasting?
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LSTMs never did beat well tuned statistical models anyway. Neural nets are only taking over forecasting in the last few years.
Re: Ask HN: Data Scientists, what libraries do you use for timeseries forecasting?
#82I like statsmodels. So far it has all methods I need, and itvis very well documented.
But I am just fiddling a little bit with my 'weather station'. No bleeding edge here.
Re: Ask HN: Data Scientists, what libraries do you use for timeseries forecasting?
#83Sktime
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Re: Ask HN: Data Scientists, what libraries do you use for timeseries forecasting?
#89For feature extraction check out tsfel:
Re: Ask HN: Data Scientists, what libraries do you use for timeseries forecasting?
#90Earlier quoted context omitted.
> - 2-3 anomalies in a row > - high deviation from expected > - multiple models all detected anomaly This is basically what statistical process control charts do for you. If you haven't learned about it already, I can recommend looking it up!
Statistical process control always seemed like some thing that would benefit me in my work, but I don't know anything about it. I have looked up random Wikipedia articles, but that's all I know. Do you know of any more "serious" learning resources in that area?
You know what? I finally bit the bullet thanks to your comment: https://news.ycombinator.com/item?id=33507217