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.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
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+.pytest_cache/
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+# Translations
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+
+# Django stuff:
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+db.sqlite3
+db.sqlite3-journal
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+# Sphinx documentation
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+# PyBuilder
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+# Jupyter Notebook
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+# IPython
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+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
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+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#uv.lock
+
+# poetry
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+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
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+#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
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+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
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+celerybeat-schedule
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+*.sage.py
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+# Environments
+.env
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README.md

@@ -1,70 +1,79 @@
-# pyarimafft library
-
-A Python Library which efficiently combines LOESS cleaning, Fast Fourier Transform Extracted key Cyclicities and ARIMA
-to produce meaningful and explainable time series forecasts.
-
-
-## Installation 
-pip install pyarimafft
-
-
-
-## Usage
-
-
-endog = np.array(vector)
-
-model_obj = pyarimafft.model(forecast_horizon=12)
-
-model_obj.outlier_clean(endog=endog,window_size=10,outlier_threshold=0.8,peak_clean=False,trough_clean=False,both_sides_clean=True)
-
-model_obj.extract_key_seasonalities(power_quantile=0.90,time_period=d)
-
-model_obj.reconstruct_seasonal_features(mode='seperate')
-
-## It is possible to add one exogenous vector at a time
-
-model_obj.add_exog(exog1)
-
-model_obj.add_exog(exog2)
-
-## Call the auto_arima function 
-
-model_obj.auto_arima(p=None,d=None,q=None,max_p=3,max_q=3,max_d=1,auto_fit=True)
-
-## Attributes which you can extract
-
-model_obj.endog
-
-model_obj.trend 
-
-model_obj.outlier_cleaned 
-
-model_obj.seasonal_component 
-
-model_obj.isolated_components 
-
-model_obj.isolated_seasonality 
-
-model_obj.forecast  
-
-model_obj.seasonal_feature_train 
-
-model_obj.seasonal_feature_future 
-
-model_obj.time_train  
-
-model_obj.time_future  
-
-model_obj.forecast_horizon
-
-model_obj.forecast 
-
-model_obj.optimal_order 
-
-'''
-
-
-
-
-
+# pyarimafft library
+
+[![PyPI Latest Release](https://img.shields.io/pypi/v/pyarimafft.svg)](https://pypi.org/project/pyarimafft/)
+[![PyPI downloads](https://static.pepy.tech/badge/pyarimafft)](https://pepy.tech/project/pyarimafft)
+[![License](https://img.shields.io/github/license/shashboy/pyarimafft)](https://github.com/shashboy/pyarimafft/blob/main/LICENSE)
+
+A Python Library which efficiently combines LOESS cleaning, Fast Fourier Transform Extracted key Cyclicities and ARIMA
+to produce meaningful and explainable time series forecasts.
+
+## Installation 
+
+```sh
+pip install pyarimafft
+```
+
+## Usage
+
+```py
+import numpy as np
+
+import pyarimafft
+
+endog = np.array(vector)
+
+model_obj = pyarimafft.model(forecast_horizon=12)
+
+model_obj.outlier_clean(
+    endog=endog,
+    window_size=10,
+    outlier_threshold=0.8,
+    peak_clean=False,
+    trough_clean=False,
+    both_sides_clean=True,
+)
+
+model_obj.extract_key_seasonalities(power_quantile=0.90, time_period=d)
+
+model_obj.reconstruct_seasonal_features(mode="seperate")
+
+## It is possible to add one exogenous vector at a time
+
+model_obj.add_exog(exog1)
+
+model_obj.add_exog(exog2)
+
+## Call the auto_arima function
+
+model_obj.auto_arima(p=None, d=None, q=None, max_p=3, max_q=3, max_d=1, auto_fit=True)
+
+## Attributes which you can extract
+
+model_obj.endog
+
+model_obj.trend
+
+model_obj.outlier_cleaned
+
+model_obj.seasonal_component
+
+model_obj.isolated_components
+
+model_obj.isolated_seasonality
+
+model_obj.forecast
+
+model_obj.seasonal_feature_train
+
+model_obj.seasonal_feature_future
+
+model_obj.time_train
+
+model_obj.time_future
+
+model_obj.forecast_horizon
+
+model_obj.forecast
+
+model_obj.optimal_order
+```

demo.py

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+import numpy as np
+
+import pyarimafft
+
+endog = np.array(vector)
+
+model_obj = pyarimafft.model(forecast_horizon=12)
+
+model_obj.outlier_clean(
+    endog=endog,
+    window_size=10,
+    outlier_threshold=0.8,
+    peak_clean=False,
+    trough_clean=False,
+    both_sides_clean=True,
+)
+
+model_obj.extract_key_seasonalities(power_quantile=0.90, time_period=d)
+
+model_obj.reconstruct_seasonal_features(mode="seperate")
+
+## It is possible to add one exogenous vector at a time
+
+model_obj.add_exog(exog1)
+
+model_obj.add_exog(exog2)
+
+## Call the auto_arima function
+
+model_obj.auto_arima(p=None, d=None, q=None, max_p=3, max_q=3, max_d=1, auto_fit=True)
+
+## Attributes which you can extract
+
+model_obj.endog
+
+model_obj.trend
+
+model_obj.outlier_cleaned
+
+model_obj.seasonal_component
+
+model_obj.isolated_components
+
+model_obj.isolated_seasonality
+
+model_obj.forecast
+
+model_obj.seasonal_feature_train
+
+model_obj.seasonal_feature_future
+
+model_obj.time_train
+
+model_obj.time_future
+
+model_obj.forecast_horizon
+
+model_obj.forecast
+
+model_obj.optimal_order