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Client library for

Tap into the collective intelligence of community contributed time series algorithms:

pip install microprediction 

This library can also be used to contribute predictions.

Class Hierarchy

The following classes are provided:

MicroWriter ----------------------------
   |                                   |
MicroPoll                         MicroCrawler
(feed creator)               (self-navigating algorithm)

Creation of feeds (to solicit predictions) is introduced below and also at

Crawling (letting loose an algorithm to explore all the time series) is explained below and also at

Quickstart: Soliciting predictions

If you have a function that returns a live number, do this:

from microprediction import MicroPoll, create_key
feed = MicroPoll(write_key=create_key(),        # This takes a while ... see section on mining write_keys below
                 name='my_stream.json',         # Name your data stream
                 func=my_feed_func,             # Provide a callback function that returns a float 
                 interval=20)                   # Poll every twenty minutes                                      # Start the scheduler

Retrieving distributional predictions

Once a stream is created and some crawlers have found it, you can view activity and predictions at,

| Stream      |   Roughly 1 min ahead           | Roughly 5 min ahead             |   Roughly 15 min ahead               |
| my_stream   | `stream=my_stream&horizon=70`   |  `stream=my_stream&horizon=310` | `stream=my_stream&horizon=910`       |

Here is an actual example: for a 1 minute ahead CDF. Programmatically:

     cdf = feed.get_cdf('cop.json',delay=70,values=[0,0.5])

where the delay parameter, in seconds, is the prediction horizon (it is called a delay as the predictions used to compute this CDF have all be quarantine for 70 seconds or more). The community of algorithms provides predictions roughly 1 min, 5 min and 15 minutes ahead of time. The get_cdf() above reveals the probability that your future value is less than 0.0, and the probability that it is less than 0.5. You can view CDFs and activity at MicroPrediction.Org.


A bonus! Based on algorithm predictions, every data point you publish creates another three streams, representing community z-scores for your data point based on predictions made at different times prior.

Base stream
Z-score relative to 70s ahead predictions
Z-score relative to 310s ahead predictions
Z-score relative to 910s ahead predictions

In turn, each of these four streams is predicted at three different horizons, as with the base stream. So there are twelve predictions in total accessed via the dashboard

Stream Roughly 1 min ahead Roughly 5 min ahead Roughly 15 min ahead
cop stream=cop&horizon=70 stream=cop&horizon=310 stream=cop&horizon=910
z1~cop~70 stream=z1~cop~70&horizon=70 stream=z1~cop~70&horizon=310 stream=z1~cop~70&horizon=910
z1~cop~310 stream=z1~cop~70&horizon=70 stream=z1~cop~70&horizon=310 stream=z1~cop~70&horizon=910
z1~cop~910 stream=z1~cop~70&horizon=70 stream=z1~cop~70&horizon=310 stream=z1~cop~70&horizon=910

Quickstart: Providing predictions

If you have a function that takes a vector of lagged values of a time series and supplies a distributional prediction, a fast way to get going is deriving from MicroCrawler as follows:

from microprediction import MicroCrawler, create_key
from microprediction.samplers import differenced_bootstrap

class MyCrawler(MicroCrawler):

    def sample(self, lagged_values, lagged_times=None, name=None, delay=None):
        my_point_estimate = 0.75*lagged_values[0]+0.25*lagged_values[1]                                     # You can do better
        scenarios = differenced_bootstrap(lagged=lagged_values,  decay=0.01, num=self.num_predictions)      # You can do better
        samples = [ my_point_estimate+s for s in scenarios ]
        return samples

my_write_key = create_key(difficulty=11)   # Be patient. Maybe visit to learn about Memorable Unique Identifiers 
crawler = MyCrawler(write_key=write_key)

Enter your write_key into to find out which time series your crawler is good at predicting. Check back in a day, a week or a month.

Read client

It is possible to retrieve most quantities at with direct web calls such as Use your preferred means such as requests or aiohttp. For example using the former:

import requests
lagged_values = requests.get('').json()
lagged        = requests.get('').json()

However the reader client adds a little convenience.

from microprediction import MicroReader
mr = MicroReader()

current_value = mr.get('cop.json')
lagged_values = mr.get_lagged_values('cop.json') 
lagged_times  = mr.get_lagged_times('cop.json')

Your best reference for the API is the client code

Write client

As noted above you may prefer to use MicroPoll or MicroCrawler rather than MicroWriter directly. But here are a few more details on the API wrapper those wanting more control. You can create predictions or feeds using only the writer. Your best reference is the client code

Instantiate a writer

In principle:

from microprediction import MicroWriter, create_key
mw = MicroWriter(write_key=create_key(difficulty=12))    # Sub in your own write_key. MUIDs explained at 

In practice you may want to run create_key() separately as it will take many hours, at least for a difficult key. See for the current values of min_len, which is the official minimum difficulty to create a stream. If you don't need to create streams but only wish to predict, you can use a lower difficulty like 10 or even 9. But the easier your key, the more likely you are to go bankrupt.

Submitting scenarios (manually)

If MicroCrawler does not suit your needs you can submit predictions:

scenarios = [ i*0.001 for i in range(mw.num_predictions) ]   # You can do better ! 
mw.submit(name='cop.json',values=scenarios, delay=70)        # Specify stream name and also prediction horizon

See for a list of values that delay can take.

Creating a feed (manually)

If MicroPoll does not serve your needs you can create your stream one data point at a time:

mw  = MicroWriter(write_key=write_key)
res = mw.put(name='mystream.json',value=3.14157) 

However if you don't do this regularly, your stream's history will die and you will lose rights to the name 'mystream.json' established when you made the first call. If you have a long break between data points, such as overnight or over the weekend, consider touching the data stream:

res = mw.touch(name='mystream.json')

to let the system know you still care.

More on mining write_keys

If you want to collect some write_keys off to the side, you can cut and paste this bash command into a bash shell:

/bin/bash -c "$(curl -fsSL"

or use the MUID library ( ...

$pip install muid
>>> import muid
>>> muid.mine(skip_intro=True)

See or for more on MUIDs. Use a URL like to reveal the hidden "spirit animal" in a MUID. The difficulty is the length of the animal, not including the space.

Maintaining your balance

Every participating write_key has an associated balance. When you create a stream you automatically participate in the prediction of the stream. A benchmark empirical sampling algorithm with some recency adjustment is used for this purpose. If nobody can do a better job that this, your write_key balance will neither rise nor fall, on average.

However once smart people and algorithms enter the fray, you can expect this default model to be beaten and the balance on your write_key to trend downwards. On an ongoing basis you also need the write_key balance not to fall below a threshold bankruptcy level. The minimum balance for a key of difficulty 9 is also found at and the formula -1.0*( abs(self.min_balance)*(16**(write_key_len-9)) ) supercedes whatever is written here. However at time of writing the bankruptcy levels are:

write_key difficulty bankruptcy write_key difficulty bankruptcy
8 -0.01 11 -256
9 -1.0 12 -4,096
10 -16.0 13 -65,536

Balance may be transfered from one write_key to another if the recipient write_key has a negative balance. You can use the transfer function to keep a write_key alive that you need for sponsoring a stream. You can also ask others to mine muids for you and contribute in this fashion, say if you have an important civic nowcast and expect that others might help maintain it. You cannot use a transfer to raise the balance associated with a write_key above zero - that is only possible by means of accurate prediction.

Advanced topic: Higher dimensional prediction with cset()

Multivariate prediction solicitation is available to those with write_keys of difficulty 1 more than the stream minimum (i.e. 12+1). If you want to use this we suggest you start mining now. My making regular calls to mw.cset( ) you can get all these goodies automatically:

Functionality Example dashboard URL
Base stream #1
Base stream #2
Bivariate copula
Trivariate copula

Copula time series are univariate. An embedding from R^3 or R^2 to R is used (Morton space filling Z-curve). The most up to date reference for these embeddings is at

Troubleshooting stream creation

  1. Upgrade the library, which is pretty fluid

    1. pip install --upgrade microprediction
  2. Check to see if you are violating a stream naming convention

    1. Must end in .json
    2. Must contain only alphanumeric, hyphens, underscores, colons (discouraged) and at most one period.
    3. Must not contain double colon.
  3. Log into Dashboard with your write_key:

    2. Check for errors/warnings You can also use mw.get_errors(), mw.get_warnings(), mw.get_confirmations()
    3. Was the name already taken?
    4. Is you write_key bankrupt?
  4. Get in touch:

    1. File an issue at if you believe there is a problem
    2. Post a question on Quora and request answer from user 'Peter Cotton' if you need advice.

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