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Prophet: forecasting at scale

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Re: Prophet: forecasting at scale

#41

Post by justonepost »

etjossem wrote: post #33
blazespin wrote: post #3

Earlier quoted context omitted.

Probably just help verify that the stock market is a random walk with a meager trend upwards that doesn't beat inflation + trading costs.

> Probably just help verify that the stock market is a random walk with a meager trend upwards that doesn't beat inflation + trading costs. That doesn't sound right. Let me clear that up for you. Since 1950: S&P 500 Annual Price Change: 7.2% S&P 500 Annual Div Dist: 3.6% S&P 500 Annual Total Return: 11.0% Annual Inflation: 3.8% Annual Real Price Change: 3.3% Annual Real Total Return: 7.0 % Buying the straight S&P 500…

You don't need prophet for that.

Re: Prophet: forecasting at scale

#42

Post by [deleted] »

matheweis wrote: post #5
blazespin wrote: post #3

Earlier quoted context omitted.

Probably just help verify that the stock market is a random walk with a meager trend upwards that doesn't beat inflation + trading costs.

> the stock market is a random walk with a meager trend upwards that doesn't beat inflation + trading costs. That assumes that the efficient-market hypothesis holds true, but it has yet to be thoroughly proven or disproven... (and funds like Medallion would strongly suggest otherwise for the medium term: https://www.bloomberg.com/news/articles/2016-11-21/how-renai... )

[deleted]

Re: Prophet: forecasting at scale

#43

Post by etjossem »

justonepost wrote: post #41
etjossem wrote: post #33

Earlier quoted context omitted.

> Probably just help verify that the stock market is a random walk with a meager trend upwards that doesn't beat inflation + trading costs. That doesn't sound right. Let me clear that up for you. Since 1950: S&P 500 Annual Price Change: 7.2% S&P 500 Annual Div Dist: 3.6% S&P 500 Annual Total Return: 11.0% Annual Inflation: 3.8% Annual Real Price Change: 3.3% Annual Real Total Return: 7.0 % Buying the straight S&P 500…

You don't need prophet for that.

:)

Re: Prophet: forecasting at scale

#44

Post by techno_modus »

recurser wrote: post #30

Very cool. Could this be re-purposed for detecting anomalies/outliers in time series data?

>Could this be re-purposed for detecting anomalies/outliers in time series data?

If you define anomaly as something unexpected then yes. In this case, if the reality differs significantly from the forecast (=expectation) then it is an anomaly (according to our definition). In numeric univariate case, there could be positive anomalies where you get more than expected, and negative anomaly where you get less than expected.

Re: Prophet: forecasting at scale

#45

Post by yoghurtio »

nodesocket wrote: post #29
yoghurtio wrote: post #26

Earlier quoted context omitted.

Its very simple to use Yoghurt, just upload the data and rest it does automatically. 24 data points is less to make any accurate prediction. You need more data points. However, Yoghurt currently supports 1 week prediction only and very soon we will be adding prediction upto 1 Month and plus.

Excuse my ignorance, but how does 1 week fit into the equation? Why does the time scale (x-axis) matter? I.E. if I pass 180 points of revenue (y-axis) does it matter if they were sampled each day or each hour in terms of forecasting?

Yes it would matter. Our algorithm(SandDune)is built around measuring data on a daily basis at this stage. It takes daily input data and predicts the next week's data on a daily basis. If you give it 180 daily data points, it will predict next 7 data points.

Re: Prophet: forecasting at scale

#46

Post by nodesocket »

yoghurtio wrote: post #45
nodesocket wrote: post #29

Earlier quoted context omitted.

Excuse my ignorance, but how does 1 week fit into the equation? Why does the time scale (x-axis) matter? I.E. if I pass 180 points of revenue (y-axis) does it matter if they were sampled each day or each hour in terms of forecasting?

Yes it would matter. Our algorithm(SandDune)is built around measuring data on a daily basis at this stage. It takes daily input data and predicts the next week's data on a daily basis. If you give it 180 daily data points, it will predict next 7 data points.

I see. Any plans to support monthly data? That is more useful for financial models.

Re: Prophet: forecasting at scale

#47

Post by JoelSanchez »

rodionos wrote: post #16

I didn't know wikipedia page view counters are available for public usage. The wikipediatrend R package relies on http://stats.grok.se/ , which in turn relies on https://dumps.wikimedia.org/other/pagecounts-raw/ which has been deprecated. The new dump is located at https://dumps.wikimedia.org/other/pageviews/ Data is available in hourly intervals. * pageviews-20170227-050000 en Peyton_Manning 58 0 [edit] There is a w…

https://tools.wmflabs.org/pageviews/?project=en.wikipedia.or...

What's up with Java? (Set "logarithmic scale" to improve the visualization)

Re: Prophet: forecasting at scale

#48

Post by anton_tarasenko »

rodionos wrote: post #16

I didn't know wikipedia page view counters are available for public usage. The wikipediatrend R package relies on http://stats.grok.se/ , which in turn relies on https://dumps.wikimedia.org/other/pagecounts-raw/ which has been deprecated. The new dump is located at https://dumps.wikimedia.org/other/pageviews/ Data is available in hourly intervals. * pageviews-20170227-050000 en Peyton_Manning 58 0 [edit] There is a w…

BigQuery also has the public dataset of Wikipedia page views. Handy for quick SQL and sampling.

An intro by Felipe Hoffa (Google): https://www.reddit.com/r/bigquery/comments/3dg9le/analyzing_...

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