Feature idea summary

Cgroups plugin supports only proportional and max Block IO policies. We should support BFQ scheduler as well. Disk stats for the scheduler are in blkio.bfq.io_service_bytes and blkio.bfq.io_serviced files.

Feature Request

support an input plugin of the following type:

[[inputs.test]]
  metrics = [
    'weather,state=ny temperature=81.3'
  ]

Proposal:

support the above

Current behavior:

possible through creating a file and using inputs.file or inputs.tail

Desired behavior:

above

Use case: <!-- [Why is this important (helps with prioritizing

it's becoming more time-consuming and error-prone to manually re-test all the demos following internal refactorings and API adjustments.

now that the API is fleshed out a bit, it's possible to test a large amount of code (non-granularly) without having to simulate all interactions via Puppeteer or similar.

a lot of code can already be regression-tested by simply running all the demos and val

add standardised type hints to classifiers, clusterers and forecasters

alan-turing-institute/sktime#928

alan-turing-institute/sktime#964

Describe the bug
Sometimes alias time does not work

To Reproduce

create table tst (
timestamp timestamp
)

select timestamp time from tst

Expected behavior
Result column name is timestamp but should be time

Screenshots

Environment (please complete the following information):

• Version: 6.0.2

Additional context
This is the workaround sy

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Use case:

• Compatibility with PostgreSQL
• Time-series applications

Feature description:

the CrateDB INTERVAL data type does not currently support the PostgreSQL input units MILLENNIUM, CENTURY, DECADE, MILLISECOND, or MICROSECOND. the larger units might not be so useful for mo

Description

(A clear and concise description of what the feature is.)

• util.cumsum implementation https://github.com/awslabs/gluon-ts/blob/master/src/gluonts/mx/util.py#L326 does not scale under mx.ndarray
• cumsum is 2-5 times slower than nd.cumsum under both mx.sym and mx.ndarray, and even fails for large 4-dim input

Sample test

Code

# import ...
def test_

Hi, Thanks for the awesome library!

So I am running a Kmeans on lots of different datasets, which all have roughly four shapes, so I initialize with those shapes and it works well, except for just a few times. There are a few datasets that look different enough that I end up with empty clusters and the algorithm just hangs ("Resumed because of empty cluster" again and again).

I conceptually