Detect the dominant period in univariate, equidistant time series data.
Project description
Periodicity Detection
Detect the dominant period in univariate, equidistant time series data.
Toolbox for detecting the dominant period in univariate, equidistant time series data. The toolbox contains the following methods:
- Autocorrelation
- AutoPeriod
- Fast Fourier Transform (FFT)
find_length- Python-adaption of the R package
forecast'sfindfrequencyfunction - Number of Peaks-method
📖 Periodicity Detection's documentation is hosted at https://periodicity-detection.readthedocs.io.
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file periodicity_detection-0.1.0rc2.tar.gz.
File metadata
- Download URL: periodicity_detection-0.1.0rc2.tar.gz
- Upload date:
- Size: 14.9 kB
- Tags: Source
- Uploaded using Trusted Publishing? Yes
- Uploaded via: twine/4.0.2 CPython/3.11.4
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
12e6899836e716e81e54090d1c83da123e6b00c3f0f72391df78a5d857bbff3e
|
|
| MD5 |
04e40d0710e450f1aa7887f33c90b2d6
|
|
| BLAKE2b-256 |
2dd2206817308ed81d5da0d7b56e1473b32f8a5be074170a9359bb3367e10b69
|
File details
Details for the file periodicity_detection-0.1.0rc2-py3-none-any.whl.
File metadata
- Download URL: periodicity_detection-0.1.0rc2-py3-none-any.whl
- Upload date:
- Size: 17.0 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? Yes
- Uploaded via: twine/4.0.2 CPython/3.11.4
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
85a9807d8b91aa5fc2ea9ece77052dbd92303df408e389802584dea1c7011ada
|
|
| MD5 |
e4adf167acd3c4b267bb179fc56ac7bc
|
|
| BLAKE2b-256 |
4585daf68e0c3bd971185a39619a966a7973d13da6828d046a3072d482b68526
|
