Can someone ELI5 what Prophet and time series are? Is there ever a chance a layperson would find use in Prophet?
Is Facebook's “Prophet” the time-series Messiah or just a naughty boy?
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Re: Is Facebook's “Prophet” the time-series Messiah or just a naughty boy?
#32Earlier quoted context omitted.
Personally I find there is one important factor, and one factor alone. 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…
Is there also "one factor alone" in predicting stock market performance? Foreign-exchange rates? Lottery numbers?
Predicting stock marketperformance, i.e. performance of individual equities ?
The TL;DR answer is "no, but...".
No because by definition there is a high probability for individual equities to display idiosyncratic behaviour. Why ? Because we are, afterall, talking about individual companies. So their stockmarket performance is inherently tied to their corporate financial performance, their corporate prospects and how investors feel about all that jazz.
The "but" comes because there are, as always, exceptions to the rule.
You can, for example, engage in momentum trading. That should be (reasonably !) simple to model with a few inputs.
Otherwise, at the other end of the complexity spectrum, you can build a model to identify stocks that are in a macro regime. When stocks are in a macro regime it means that they are behaving as a proxy for macroeconomics instead of the individual usual corporate measures. This means you can build your model based on real quantitative measures (i.e. suitable macro factors) instead of trying to second guess idiosyncratic stock behaviour. The only real downside is that you will need access to quality macro data feeds, so if you are thinking of doing this as a retail investor (i.e. private individual) you might find yourself falling at the first hurdle.
Re: Is Facebook's “Prophet” the time-series Messiah or just a naughty boy?
#33Facebook also recently put out a toolkit for time series analysis called Kats https://facebookresearch.github.io/Kats/
Anyone finding the change point detection features of Kats useful?
Re: Is Facebook's “Prophet” the time-series Messiah or just a naughty boy?
#34I'm a professional forecaster (i.e. getting paid for it) at a large e-commerce company. We have extensive experience with Prophet and a host of other approaches (all the traditional models in Hyndman's book/R package, some scattered LSTM/NN implementations). Here's my quick take (the article is a lot more extensive than the median blogpost, and likely warrants a more extensive study than I have time for right now.) P…
> If you want to predict (multiple) time series using multiple series as input/predictors, that's a whole new level of difficulty. I don't know of a good automatic/fast/scalable approach that properly guards against overfitting
Have you had a look at algorithms contained in pytorch forcasting? https://pytorch-forecasting.readthedocs.io/en/latest/
Re: Is Facebook's “Prophet” the time-series Messiah or just a naughty boy?
#35Can someone ELI5 what Prophet and time series are? Is there ever a chance a layperson would find use in Prophet?
Time series are numbers measured or aggregated at some consecutive times or days. It's a data type with a specific structure, like a set, a vector or a matrix: what's inside varies a lot and depends on the specific application (e.g. daily temperature, rainfall, stock prices, items sold, water level in a river, corona infections).
Prophet is a Python library that implements a specific type of extrapolation model to predict the future: i.e. you input a few months/years of historical daily sales figures, it tries to detect trends and recurring patters, and it outputs predictions of future daily sales. It's currently one of the most popular approaches, probably because it's pretty easy to use and a good fit for many time series in commercial companies. The article shows a lot of of examples of time series where the approach does not work well.
If the layperson doesn't have to produce forecasts for their job, (s)he probably has little use for Prophet (or other statistical forecasting models).
Re: Is Facebook's “Prophet” the time-series Messiah or just a naughty boy?
#36Can someone ELI5 what Prophet and time series are? Is there ever a chance a layperson would find use in Prophet?
If you have historical data with timestamps and you want to predict how that data trends in the future, you can apply statistical methods yourself, or train a neutral net, or use an off-the-shelf package like Prophet.
Whether a layman finds it useful depends on the data, what they're used for, how well they're suited to Prophet's strengths, how lay the man is. I imagine by the time a layman gets to the point of understanding its strengths and weaknesses, she will no longer be quite a layman
Re: Is Facebook's “Prophet” the time-series Messiah or just a naughty boy?
#37Can someone ELI5 what Prophet and time series are? Is there ever a chance a layperson would find use in Prophet?
TL;DR: Time series data are measurements ordered by time. Time series analysis tools like Prophet tries to guess future values based on past ones. This matters because many economic decisions take future values as inputs, and we can't measure the future values yet.
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Time series are data values ordered by time. So amount of rain for each year 1993--2017, or the sales each week of Q1 this year, or service time for each request received the previous minute, or ... you can come up with your own examples.
The reason for ordering by time is that we think that there might be patterns in the data that are revealed over time. The measurements might be evolving in (somewhat) predictable ways, day by day, or year by year.
Say, then, you know how much sunshine your area received per year between 1999 and 2020. You're thinking of installing solar panels. Then you have an interesting problem at your hands: given what you know about sunshine historically, how much sunshine will you receive in this year, 2021? How about 2022? What can you reasonably expect to get out of your solar panels in the future?
This is probably the most obvious way to apply Prophet: I have an economic decision to make, which takes as input future data. Obviously I don't have future data yet, so I need a good guess based on the historic data I have. Prophet attempts to make that guess.
Re: Is Facebook's “Prophet” the time-series Messiah or just a naughty boy?
#38Earlier quoted context omitted.
Does it though? The article explicitly shows cases where simple last-value performs better.
Yes, it does. I'm not sure what the author is trying to prove, but he's feeding a bunch of non-seasonal data into a highly seasonal modeling technique, and then hurling insults when it doesn't work because that's not what it was originally designed for. I'm sorry, but it just seems childish.
But this seems to be a bit contradictory. Both facebook and you claim that prophet is a package to easily do better than other simple approaches, thus providing at least a good baseline for applied data people.
By contrast, the article suggests that prophet does badly in many common forecasting situations. So while prophet may do fine with piecewise linear seasonal data, it apparently is far from a good default.
There’s an inconsistency in the message here. Is prophet a tool for a very specific type of time series? Or, as the marketing claims go, is it a general easy to use default?
Much like facebook, you made both claims successively, which is I think the point of the article.
Re: Is Facebook's “Prophet” the time-series Messiah or just a naughty boy?
#39Of course Prophet is a Python library, thus the Monty Python's Life of Brian reference:
"Now you listen here. He's not the messiah. He's a very naughty boy. Now GO AWAY!"
https://www.youtube.com/watch?v=3_kKAeh6qyc&ab_channel=dinge...
"Yes, we are all individuals!" "Yes, we are all different!" "I'm not.":
https://www.youtube.com/watch?v=KHbzSif78qQ&ab_channel=lucas...
Prophets:
https://www.youtube.com/watch?v=hmyuE0NpNgE&ab_channel=radia...
Life of Brian:
https://en.wikipedia.org/wiki/Monty_Python%27s_Life_of_Brian
Not the Messiah (He's a Very Naughty Boy):
https://en.wikipedia.org/wiki/Not_the_Messiah_(He%27s_a_Very...
Re: Is Facebook's “Prophet” the time-series Messiah or just a naughty boy?
#40I'm a professional forecaster (i.e. getting paid for it) at a large e-commerce company. We have extensive experience with Prophet and a host of other approaches (all the traditional models in Hyndman's book/R package, some scattered LSTM/NN implementations). Here's my quick take (the article is a lot more extensive than the median blogpost, and likely warrants a more extensive study than I have time for right now.) P…
> If you want to predict (multiple) time series using multiple series as input/predictors, that's a whole new level of difficulty. I don't know of a good automatic/fast/scalable approach that properly guards against overfitting Have you had a look at algorithms contained in pytorch forcasting? https://pytorch-forecasting.readthedocs.io/en/latest/
We only did a few internal NN and LSTM implementations in the past, we should probably evaluate the new pytorch stuff soon. But as you can imagine a lot of our time was consumed by modelling pandemic-induced dynamics (which is especially at longer forecast horizons are much more driven by assumptions rather than by data/models).