Implements a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly and weekly seasonality, plus holidays. It works best with daily periodicity data with at least one year of historical data. Prophet is robust to missing data, shifts in the trend, and large outliers.
2 maintainers
Metadata
2 maintainers
Release files for fbprophet 0.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| fbprophet-0.2.tar.gz | 31.0 kB | Without provenance | Details |
Release files / fbprophet-0.2.tar.gz
| Download URL | fbprophet-0.2.tar.gz |
|---|---|
| Size | 31.0 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
da247d103e5c5aec8ea46ee5a3a56855c9e7c87ba744511e40ec859ac914d463
|
|
BLAKE2b-256 checksum How to use checksums |
3075aea84d0c83a86e496cd57d3e9b08d1b3944eab0cc0dd009347a3fd1b4420
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
Security for fbprophet 0.2
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
