Metadata-Version: 2.1
Name: pycatcher
Version: 0.0.12
Summary: This package identifies the day-level time-series outliers for a given dataset
Author-email: Aseem Anand <aseemanand@gmail.com>
Maintainer-email: Jagadish Pamarthi <jagadish.vrsec@gmail.com>
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: pandas==2.2.3
Requires-Dist: statsmodels==0.14.4
Requires-Dist: pyod==2.0.2
Requires-Dist: seaborn==0.13.2
Provides-Extra: dev
Requires-Dist: pip>=24.2; extra == "dev"
Requires-Dist: build>=1.2.2; extra == "dev"
Requires-Dist: twine>=5.1.1; extra == "dev"
Requires-Dist: poetry>=1.8.3; extra == "dev"
Requires-Dist: setuptools>=75.1.0; extra == "dev"
Requires-Dist: pytest>=8.3.3; extra == "dev"
Requires-Dist: pytest-mock>=3.14.0; extra == "dev"
Requires-Dist: coverage>=7.6.1; extra == "dev"
Requires-Dist: prospector>=1.12.1; extra == "dev"
Requires-Dist: notebook>=7.2.2; extra == "dev"
Provides-Extra: test
Requires-Dist: pytest-cov>=5.0.0; extra == "test"

## Outlier Detection for Time-series Data
This package identifies the day-level time-series outliers for a given dataset. 
#### DataFrame Arguments:
First column in the dataframe must be a date column ('YYYY-MM-DD') and the last column a count column.
#### Package Functions:
* detect_outliers(df): Detect outliers in a time-series dataframe using seasonal trend decomposition when there is at least 2 years of data, otherwise we can use Interquartile Range (IQR) for smaller timeframe.
* detect_ouliers_today(df) Detect outliers for the current date in a time-series dataframe.
* detect_outliers_latest(df): Detect latest outliers in a time-series dataframe.
* find_outliers_iqr(df): Detect outliers in a time-series dataframe when there's less than 2 years of data.

#### Diagnostic Plots:
* built_seasonal_plot(df): Build seasonal plot (additive, multiplicative, IQR) for a given dataframe.
* build_monthwise_plot(df): Build month-wise plot for a given dataframe.
* build_decomposition_results(df): Get seasonal decomposition results for a given dataframe.
* conduct_stationarity_check(df): Conduct stationarity check (trend) for a feature (dataframe's feature or count column).





