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Inexperienced stakeholders don't want/like to see smooth forecasts, even if you provide predication intervals.
A few lines of code gets you a fitted model and some insightful plots. From these you can see if 1) it's doing great and you don't need to spend hours training some crazy transformer model, 2) It's got some flaws and you should maybe try something else (which you can now compare against fbprophet as a baseline) or 3) This data is crazier than I thought, maybe we should rethink things...
TLDR: It's easy to throw this at a new forecasting problem, and although it isn't perfect (as the article shows) sometimes it is still a useful step IMO.
I think that you can throw any model you like at the problem but all you will do is overfit most of the time.
Earlier quoted context omitted.
What would you suggest is the most effective time series analysis library?
SoTA in 2021 is the bespoke transformer model you implement yourself based on the idiosyncrasies in your data. Unless a lot of money is on the line, that is overkill for most situations though.
Their findings are very much in line with my experience with fbprophet, it is usually the least effective lib to predict a time series in my tests.
What would you suggest is the most effective time series analysis library?
https://arxiv.org/pdf/1912.09363v2.pdf
Available as pytorch implementation:
https://pytorch-forecasting.readthedocs.io/en/latest/index.h...
My experience in general is that most time series model are inadequate in predicting time series except for very trivial cases of seasonalities or simple linear/nonlinear trends. I think that you can throw any model you like at the problem but all you will do is overfit most of the time.
Context.
Seasonality is the context that there's a seasonal driver at play.
The context of a public holiday can explain a decrease in sales on that day. The context of a football match can explain a spike in transport demand near a stadium. The context of the presence of a heat dome predicted by pressure data can explain record temperature figures in Canada. The context of reopening of schools explains a spike in Covid cases after months of decreases.
The algorithm, is ultimately, not the deciding factor. The choice of what context you feed the algorithm as inputs is the real secret. Which leads to domain knowledge and an underfit linear regression beats a fancy algorithm trained on historical data of a single variable every time. Because the domain knowledge tells you what context to feed the model in the first place which is 90% of the battle.
In terms of performance, it has been the best for a few of our forecasts, compared to GRUs, LSTM, ARIMA and SARIMA. When it wasn't the best, it wasn't too far from the best model. But, to be fair, our forecast are of quite stable data, so most models do well.
However, I would say that the key strength of Prophet is how easy it is. You can produce results really fast, you can throw data with missing range, holidays, and it has interpretability components out of the box. It depends on what do you need, but for most of our tasks, we and our stakeholders are more than happy to sacrifice a bit of performance for this features.
I work with highly seasonal data (city-wide water consumption) and Prophet has been a great tool for us. In terms of performance, it has been the best for a few of our forecasts, compared to GRUs, LSTM, ARIMA and SARIMA. When it wasn't the best, it wasn't too far from the best model. But, to be fair, our forecast are of quite stable data, so most models do well. However, I would say that the key strength of Prophet i…