TimeSeriesAnalysiswithPython | Time Series Analysis with Python | Time Series Database library

by rouseguy HTML Version: Current License: MIT

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kandi X-RAY | TimeSeriesAnalysiswithPython Summary

TimeSeriesAnalysiswithPython is a HTML library typically used in Database, Time Series Database applications. TimeSeriesAnalysiswithPython has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

Time Series Analysis with Python

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TimeSeriesAnalysiswithPython has a low active ecosystem.

It has 321 star(s) with 222 fork(s). There are 35 watchers for this library.

It had no major release in the last 6 months.

TimeSeriesAnalysiswithPython has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of TimeSeriesAnalysiswithPython is current.

Quality

TimeSeriesAnalysiswithPython has no bugs reported.

Security

TimeSeriesAnalysiswithPython has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

TimeSeriesAnalysiswithPython is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

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TimeSeriesAnalysiswithPython releases are not available. You will need to build from source code and install.

Installation instructions are not available. Examples and code snippets are available.

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TimeSeriesAnalysiswithPython Key Features

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TimeSeriesAnalysiswithPython Examples and Code Snippets

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Community Discussions

Trending Discussions on TimeSeriesAnalysiswithPython

Get better fit on test data using Auto_Arima

QUESTION

Get better fit on test data using Auto_Arima

Asked 2020-May-19 at 04:43

I am using the AirPassengers dataset to predict a timeseries. For the model I am using, I chosen to use auto_arima to forecast the predicted values. However, it seems that the chosen order by the auto_arima is unable to fit the model. The corresponding chart is produced.

What can I do to get a better fit?

My code for those that want to try:

...

ANSWER

Answered 2020-May-19 at 04:31

The problem was that I did not specify the m, in this case, I assigned the value of m to be 12, denoting that it is a monthly cycle, that each data row is a month. That's how I understand it. source

Feel free to comment, I'm not entirely sure as I am new to using ARIMA.

Code:

Source https://stackoverflow.com/questions/61883120

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install TimeSeriesAnalysiswithPython

You can download it from GitHub.

Support

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