clean up

CONTRIBUTE.md

@@ -14,7 +14,7 @@ You may ask yourself, "Well, how did I get here?" And you may ask yourself, "How
 
 <img src="https://github.com/microprediction/timemachines/blob/main/images/talking_heads.jpeg" alt="drawing" width="650"/>
 
-But enough 80's rock. Chances are you're here because you reached out to connect on Linked-In, and you have some manner of time-series or quantitative interest, so I sent you an invite. Stop what you are doing. Open this [notebook](https://github.com/microprediction/microprediction/blob/master/submission_examples_die/first_submission.ipynb) and run it. The [README](https://github.com/microprediction) will make more sense, and perhaps too the notion of collective autonomous prediction.   
+But enough 80's rock. 
 
 ## Specific goals 
 The strategy here:   
@@ -40,55 +40,15 @@ The strategy here:
 
 # Contribution Patterns
 
-I suppose it is nice if people follow, clap, share, heckle on [medium](https://microprediction.medium.com/), [linked-in](https://www.linkedin.com/company/65109690) if that helps bring in contributors. Thanks. I suppose you can star, fork, watch [timemachines](https://github.com/microprediction/timemachines) or even sign this tongue-in-cheek [petition](https://www.change.org/p/towards-data-science-have-towards-data-science-publish-an-article-critical-of-facebook-software) - unless you want a job at Facebook or Towards Data Science, some day :) But here's how you can really help, even if you are new to open source...
-
+I suppose it is nice if people follow, clap, share, heckle on [medium](https://microprediction.medium.com/), [linked-in](https://www.linkedin.com/company/65109690) if that helps bring in contributors. Thanks. I suppose you can star, fork, watch [timemachines](https://github.com/microprediction/timemachines). 
 ## Creating colab notebooks illustrating the use of Python timeseries packages
 It helps speed the creation of autonomous algorithms, and Elo ratings, to have example notebooks for python time-series packages
 
    0. See [good first issues](https://github.com/microprediction/timemachines/issues).
       Or search the same link for "Create colab notebook"
 
-It's also not a bad way to familiarize yourself with packages that might be useful. No need to limit yourself to the ones in the issues. Anything that can predict k-steps ahead is fair game. See the [long list of packages](https://www.microprediction.com/blog/popular-timeseries-packages)
-
-## Running scripts
-
-Contributing compute:
-   1. Cut and paste a bash command to drive the default "crawler". See [CONTRIBUTE_COMPUTE_LOCAL_ONE_LINE.md](https://github.com/microprediction/timemachines/blob/main/CONTRIBUTE_COMPUTE_LOCAL_ONE_LINE.md). Run a Python script directly if you prefer. See [CONTRIBUTE_COMPUTE_LOCAL](https://github.com/microprediction/timemachines/blob/main/CONTRIBUTE_COMPUTE_LOCAL.md). Or run a Python script on a PythonAnywhere account that drives a "crawler". See [CONTRIBUTE_COMPUTE_PA](
- https://github.com/microprediction/timemachines/blob/main/CONTRIBUTE_COMPUTE_PA.md)
-   2. Cut and paste a bash command to burn some rare Memorable Unique Identifiers, and donate them. See [CONTRIBUTE_COMPUTE_MUIDS.md](https://github.com/microprediction/timemachines/blob/main/CONTRIBUTE_COMPUTE_MUIDS.md)
-
- ## Contribution to crawler creation  
-
-   3. Create any kind of Python crawler. Run it. Improve it. Repeat. See the [knowledge center](https://www.microprediction.com/knowledge-center) tutorials.
-   4. Create any kind of crawler, not in Python. There's less support for that, but see the [public api](https://www.microprediction.com/public-api) and Google search (for "microprediction client Julia", for example, or "micropredciction client typescript). 
-
-## Contribution to the timemachines package
-Open issues:
-
-   5. See [good first issues](https://github.com/microprediction/timemachines/issues)
-
-New package inclusion and approaches
+New package inclusion and approaches:
 
    6. See [CONTRIBUTE_BATCH_STYLE_MODELS](https://github.com/microprediction/timemachines/blob/main/CONTRIBUTE_BATCH_STYLE_MODELS.md) to add new functionality using non-incremental methods.
    7. See [CONTRIBUTE_ONLINE_STYLE_MODELS](https://github.com/microprediction/timemachines/blob/main/CONTRIBUTE_ONLINE_STYLE_MODELS.md) to add new functionality using incremental methods.
-
-## Contribution of live data
-Add live data that feeds the Elo ratings, and live contests too. 
-
-   8. See [CONTRIBUTE_LIVE_DATA.md](https://github.com/microprediction/timemachines/blob/main/CONTRIBUTE_LIVE_DATA.md)
-
-# Friday chats
-
-  - Grab the [slack invite](https://microprediction.github.io/microprediction/slack.html)
-  - Turn up to one of the informal chats we have every Friday noon EST. [meet](https://microprediction.github.io/microprediction/meet.html)
-
-But if you are shy that's fine too. I look forward to your pull requests, or seeing you on the leaderboard. Crawling can be completely anonymous, by the way. 
-
-## Career advice? 
-
-Some fraction of you were asking about career advice. There are people in the microprediction slack who can probably give better advice than me. Hassle them, but mine would be:
-
- - Take the time to learn how to contribute to open-source and do all your hobby projects in the open, on GitHub.  
- - Read the [Mathematics subject classification](https://en.wikipedia.org/wiki/Mathematics_Subject_Classification) and slowly, over time, familiarize yourself with the key seminal tricks in each area. Even if you expect to spend most of your time in [4.2.1](https://en.wikipedia.org/wiki/Computer_science#Artificial_intelligence) this will give you angles on problems that other's don't have.  
-
-I fear my other advice mostly overlaps with platitudes. 
+

CONTRIBUTE_ONLINE_STYLE_MODELS.md

@@ -14,7 +14,7 @@ If the package assumes you have to fit every new data point (like Prophet or man
 
 ### How to contribute 
 
-1. Join slack (invite [here](https://www.microprediction.com/knowledge-center))
+1. (Optional) Join crunch discord (invite [here](https://github.com/microprediction/monteprediction/blob/main/TFRO.md))
 2. Grok the package you think should be in. Create an example colab notebook (like [examples here](https://github.com/microprediction/timeseries-notebooks)) that uses the package. It should show how to produce a k-vector of 1..k step ahead predictions. You'd be surprised at how many packages seem to think this is an obscure use case and don't include it in their README :)
 
 At this point you've already helped a lot. If you want to take it all the way...
@@ -65,11 +65,8 @@ The directory [simple](https://github.com/microprediction/timemachines/tree/main
 
    - Your function must return a list or vector x of length k where x[0] is 1 step ahead, x[1] is 2 steps ahead and so forth. Ideally this is done in 
    a fast, incremental manner. Every time a number arrives the predictions for the next k are spat out. It is okay to create skaters that are slow and
-   use packages that are designed for more one-off tabular use - since it is helpful to be able to benchmark fast skaters against slow ones. However I would suggest
-   trying out some of the packages in the "online" section of the package list (see [Popular Python Time-Series Packages](https://www.microprediction.com/blog/popular-timeseries-packages)). For
-   instance state space models or online libraries like river seem promising.  
-
-
+   use packages that are designed for more one-off tabular use - since it is helpful to be able to benchmark fast skaters against slow ones.
+
    - Your function must also return a second list w that will be interpreted (loosely) as a 1-standard deviation error in the skater's forecast. It 
    is not absolutely necessary to fret about this. Some skaters just return [1 1 1 ... 1]. However, it is just a couple of lines of code to include
    a skater's own empirical estimate of its own accuracy and this is extremely important to do if you want your skater to be included in 

README.md

@@ -43,13 +43,13 @@ See [docs/interface](https://microprediction.github.io/timemachines/interface) f
 
 ### Contributions and capstone projects
 
+- See [TFRO.md](https://github.com/microprediction/monteprediction/blob/main/TFRO.md) 
 - See [CONTRIBUTE.md](https://github.com/microprediction/timemachines/blob/main/CONTRIBUTE.md) and [good first issues](https://github.com/microprediction/timemachines/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22). 
 - See the suggested steps for a [capstone project](https://microprediction.github.io/timemachines/capstone.html). 
 
 ### Getting live help
 
-- [FAQ](https://github.com/microprediction/timemachines/blob/main/FAQ.md). 
-- See the Slack invite on my user page [here](https://github.com/microprediction/slack). 
+- [FAQ](https://github.com/microprediction/timemachines/blob/main/FAQ.md).
 - Office hours [here](https://github.com/microprediction/meet). 
 - Learn how to deploy some of these models and try to win the [daily $125 prize](https://www.microprediction.com/competitions/daily).
 

docs/articles.md

@@ -9,8 +9,6 @@ View as [web page](https://microprediction.github.io/timemachines/uses) or [sour
 - [Does Wiggling Make Time-Series Models More Regular?](https://microprediction.medium.com/smooth-move-does-wiggling-make-time-series-models-less-accurate-8544e675873)
 - [Chasing StatsModel.AutoARIMA residuals in two lines of code](https://microprediction.medium.com/chasing-statsforecast-autoarima-residuals-in-two-lines-of-code-8a39c8c2561f)
 - [Combining PyCaret and Timemachines for Time-Series Prediction](https://microprediction.medium.com/combining-pycaret-and-timemachines-for-time-series-prediction-a4d456e47cd9)
-- [Predicting, Fast and Slow](https://www.microprediction.com/blog/timemachines)
-- [Fast Python Time-Series Forecasting](https://www.microprediction.com/blog/fast)
 
 
 ### Indirectly related / suggestive