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Exponential Smoothing: faster and more accurate than NeuralProphet

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Re: Exponential Smoothing: faster and more accurate than NeuralProphet

#41

Post by beernet »

tomwphillips wrote: post #40
beernet wrote: post #12

Earlier quoted context omitted.

It's true simply because large ANNs have a higher capacity, which is great for large, nonlinear data but less so for small datasets or simple functions. In any case, Transformers are eating ML right now and I'm actually surprised there's no "GPT-3 for time series" yet. It's technically the same problem as language modeling (that is, multi-step prediction of numerics), however, there is only a comparably little amount…

Like I said, still not seen any evidence.

Then look at some of the past time series related Kaggle challenges, plenty of evidence there in the winning solutions.

Re: Exponential Smoothing: faster and more accurate than NeuralProphet

#42

Post by disgruntledphd2 »

maxmc wrote: post #30
Imnimo wrote: post #7

In the original Prophet paper ( https://peerj.com/preprints/3190.pdf ) they claim that Prophet outperforms ETS (see Figure 7, for example). And in the NeuralProphet paper, they claim that it outperforms Prophet (but do not, as far as I can see, compare directly to ETS). Here we see ETS outperforms NeuralProphet. Presumably this apparent non-transitivity is because of differences in each evaluation. If we fix the eval…

Figure 7 of the mentioned paper evaluates FB-Prophet in a extremely convenient environment of long horizon h in {30,60,90,120,150,180}. It is known that ETS and ARIMA models concatenate errors and degrade in performance with longer forecasting horizons. We have explored and offered solutions to these issues with the N-HiTS model specialized in long-horizon ( https://arxiv.org/abs/2201.12886 ). In recent years, FB-Pro…

Prophets original use case was for forecasting extremely long time series rather automatically. If that's not your use case, it may not be the right fit.

Re: Exponential Smoothing: faster and more accurate than NeuralProphet

#43

Post by time_to_smile »

throwawaythekey wrote: post #36
time_to_smile wrote: post #16

Earlier quoted context omitted.

A larger problem is that time series modeling is particularly resistant towards black box approaches since a lot of information is encoded in the model itself. Take even a simple moving average model on daily observations. Consider stock ticker data (where there are no weekends) and web traffic data (where there is an observation each day). The stock ticker data should be smoothed with a 5 day window and the web traf…

Do you know of any good resources that explain why smoothing should be used? My intuition is that in your given example (stock prices) smoothing would probably be doing yourself a disservice as it would hide the optimal hour or day of the week to make purchases/sales. Is it mostly related to the timeframe of your analysis and needing to trade off near term precision for longer term precision?

Smoothing is used to negate seasonal effects.

Suppose you run a bar, and your busiest days in order are Friday, Saturday, Thursday, Sunday, Wednesday, Tuesday and Monday.

Now you are the owner and you want to look at your foot traffic everyday to monitor the health of your business. However, from the ordering I've presented, this will almost never work trivially. Monday traffic will always be less than Sunday, does this mean every Monday you should be concerned about business? Of course not.

However by averaging the last 7 days and looking at that each day, you are canceling out these seasonal effects because every single day of the week is accounted for in your measurement. If the 7 day moving average on Monday is less than Sunday you should be concerned because the average when calculated on Sunday included the pervious Monday.

For your example, you use the smoothed data and a history of the original data to come up with an exact explanation of which days are the best.

For example if you are a bar owner and you don't know which day is the best, you can take a 7 day moving average and subtract it from each day of actual observations. Then averages those differences grouped by day of week and you get an estimate for the day of week effect (you can also calculate standard deviation as well).

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