time-series

Hi,

first thanks for this awesome software. But I have some trouble and I don't know how to proceed. First I try to find out what else I could provide for this report to be a good bug report.

First, I use the latest version (v1.14.0) from openSUSE build Service. I know, I should compile it by my self to avoid any other causes for this behavior. But for now I still use this package from there

https://github.com/taosdata/TDengine/blob/develop/src/client/src/tscParseLineProtocol.c

val_s <= 9223372036854775807L is always true regardless of the values of its operands. This occurs as the logical second operand of "&&".

Relevant telegraf.conf:

[[inputs.openweathermap]]
  app_id = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
  

The goal is to create thin, easy to use PHP client for sending ILP messages. We already have a Java client:

• https://github.com/questdb/questdb/blob/master/core/src/main/java/io/questdb/cutlass/line/AbstractLineSender.java
• https://github.com/questdb/questdb/blob/master/core/src/main/java/io/questdb/cutlass/line/LineTcpSender.java
• https://github.com/questdb/questdb/blob/master/core/src/ma

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

Please add LinkedIn Greykite time series model as a part of sktime.

Add alpha value in the dataframe that is returned when predicting

See this comment link

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提建议？
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提修复PR？
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提Feature？
建议先将想法提出，不着急写代码，与社区同道沟通，大家觉得OK再做代码实现

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另外，我们是在做开源软件，不是服务于您的企业，做您的乙方，您一分钱没付，就不要颐指气使了，认同我们这个社区，则参与进来，不认同，就不参与，谢谢理解

Use case:

Adding an array element to an existing array inside the database without the need to select the array first and update the full array value afterwards.

CREATE TABLE t1 (id INTEGER, tags ARRAY(TEXT));
INSERT INTO t1 (id, tags) VALUES (1, ['database']);

UPDATE t1 SET
  tags = array_append(excluded.tags, 'search engine');
WHERE id = 1;

**Feature descr

Recently found your library and it is really great and handy!
The evaluation of the forecasting performance is best done using a cross-validation approach. The backtest_forecasting()-function does that - although it currently iterates and re-trains the model on every single time step. In my application, I am training ten-thousands of different time series and it becomes computationally unfeasib

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