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.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+__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
+DynamicTS.egg-info
+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
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# 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:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# UV
+#   Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#uv.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   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
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
+.pdm.toml
+.pdm-python
+.pdm-build/
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.myenv
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+# Abstra
+# Abstra is an AI-powered process automation framework.
+# Ignore directories containing user credentials, local state, and settings.
+# Learn more at https://abstra.io/docs
+.abstra/
+
+# Visual Studio Code
+#  Visual Studio Code specific template is maintained in a separate VisualStudioCode.gitignore 
+#  that can be found at https://github.com/github/gitignore/blob/main/Global/VisualStudioCode.gitignore
+#  and can be added to the global gitignore or merged into this file. However, if you prefer, 
+#  you could uncomment the following to ignore the enitre vscode folder
+# .vscode/
+
+# Ruff stuff:
+.ruff_cache/
+
+# PyPI configuration file
+.pypirc
+
+# Cursor
+#  Cursor is an AI-powered code editor. `.cursorignore` specifies files/directories to
+#  exclude from AI features like autocomplete and code analysis. Recommended for sensitive data
+#  refer to https://docs.cursor.com/context/ignore-files
+.cursorignore
+.cursorindexingignore
+
+
+/data
+data
+/data/
+data/
+
+
+# Directories to ignore:
+sample_data/
+
+ignore
+/ignore
+ignore/
+/ignore/
+
+logs
+metadata
+**/reports/
+
+/logs
+/metadata
+reports/
+=======
+metadata/
+logs/
+**/metadata/
+**/logs/
+
+
+# Files to ignore:
+test.ipynb
+**/.gitignore
+
+**/.DS_Store/
+
+
+dynamicts.egg-info
+

README.md

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----
-license: mit
----
+# DynamicTS
+
+## What is DynamicTS?
+
+**DynamicTS** is an educational-first Python library for time series analysis and preprocessing.  
+It is designed to make time series workflows easy to use, easy to interpret, and easy to teach.  
+DynamicTS provides clear, automated reports and visualizations that help users not only run analyses, but also understand and explain the results.
+
+Whether you are a student, educator, or practitioner, DynamicTS aims to bridge the gap between powerful time series methods and accessible, readable outputs.  
+With a focus on transparency and best practices, DynamicTS is your companion for learning, teaching, and applying time series analysis in Python.
+A Python library for time series analysis.
+
+## Main Features
+
+DynamicTS is built to make time series analysis accessible and insightful by combining robust analytics with clear, user-friendly reporting. Each module is designed to automate best practices and generate readable reports that help users interpret results with confidence.
+
+- **Data Loader Module**  
+  Load your time series data from CSV files with robust handling of date parsing and index regularity checks. The loader automatically validates your data, checks for  irregular timestamps, and saves metadata reports for easy reference.
+2
+- **Analysis Module**  
+  Perform comprehensive univariate analysis on your time series data. This module automatically generates distribution plots, missing value checks and outlier detection. All results are compiled into a well-structured Markdown or HTML report, making it easy to review and share insights.
+
+- **Lag Correlation Module**  
+  Explore autocorrelation and partial autocorrelation with ACF and PACF plots, as well as lag matrix visualizations. The module summarizes findings in a report, highlighting significant lags and patterns to help you understand temporal dependencies in your data.
+
+
+- **Stationarity Module**  
+  Assess the stationarity of your time series using statistical tests like the Augmented Dickey-Fuller (ADF) test and rolling statistics. The module generates clear visualizations and a summary report, guiding you through the interpretation of stationarity results and next steps.
+
+- **Volatility Check Module**  
+  Assess the volatility and variability of your time series using advanced models such as ARCH and GARCH. This module applies these models to detect and visualize periods of changing volatility, helping you understand the dynamic behavior of your data. Results are presented through intuitive plots and a concise, user-friendly report, making it easy to interpret volatility patterns and their implications for stationarity.
+
+All modules are designed to output their results as readable, shareable reports (Markdown or HTML) saved next to your data file by default. This ensures that every analysis is not only rigorous, but also easy to understand and communicate.
+
+## Where to Get DynamicTS
+
+You can easily install DynamicTS using pip from the Python Package Index (PyPI):
+
+```sh
+pip install dynamicts
+```
+
+PyPI page: [https://pypi.org/project/dynamicts/](https://pypi.org/project/dynamicts/)
+
+Alternatively, you can get the latest development version directly from GitHub:
+
+```sh
+pip install git+https://github.com/Chinar-Quantum-AI-Ltd/DynamicTS.git
+```
+
+GitHub repository: [https://github.com/Chinar-Quantum-AI-Ltd/DynamicTS](https://github.com/Chinar-Quantum-AI-Ltd/DynamicTS)
+
+## Maintainers
+
+### Cultivating AI Excellence
+
+DynamicTS is maintained by [Chinar Quantum AI (CQAI)](https://chinarqai.com), an organization dedicated to advancing AI education and workforce readiness.
+
+At CQAI, we believe in training differently to address the exponential growth of the AI industry and the evolving demands of the job market. Our tailored training programs, rooted in first principles, bridge the gap between diverse backgrounds and industry requirements. By focusing on generative AI, we empower individuals to meet the industry's burgeoning demands and play a significant role in mitigating the global unemployment crisis.
+
+Our approach is multifaceted, with strategic objectives such as industry-grade projects, comprehensive mathematical and computational training, and partnerships with various institutions. Through these initiatives, we democratize AI education, empowering individuals from diverse backgrounds to pursue rewarding careers in AI and Data Science.
+
+Learn more about our mission and programs at [chinarqai.com](https://chinarqai.com).
+
+## Dependencies
+
+DynamicTS relies on a set of well-established Python libraries to provide robust time series analysis and reporting:
+
+- **pandas**: For data manipulation and handling time series data structures.
+- **numpy**: For efficient numerical computations and array operations.
+- **matplotlib**: For generating high-quality plots and visualizations.
+- **statsmodels**: For advanced statistical modeling and time series analysis, including stationarity tests.
+- **seaborn**: For enhanced statistical data visualization.
+- **pytest (>=7.4.0)**: For running the test suite and ensuring code reliability.
+- **IPython**: For interactive computing and improved notebook integration.
+- **arch (==7.2.0)**: For volatility modeling using ARCH and GARCH models.
+
+These dependencies ensure that DynamicTS delivers a comprehensive, reliable, and user-friendly experience for time series analysis and education.

__init__.py

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+__version__ = '0.1.0'

pyproject.toml

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+[build.system]
+requires = ['setuptools>=42.0', 'wheel']
+build-backend = "setuptools.build_meta"
+
+[tool.pytest.ini_options]
+testpaths = [
+    "tests"
+    ]
+
+[tool.mypy] 
+mypy_path = "dynamicts"
+ignore_missing_imports = true 

requirements.txt

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+pandas
+numpy
+matplotlib
+statsmodels
+seaborn
+pytest>=7.4.0
+IPython
+arch

requirements_dev.txt

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+pandas
+numpy
+matplotlib
+statsmodels
+seaborn
+IPython
+arch
+
+# Development Requirements -
+tox==3.25.1
+black==22.8.0
+flake8>=5.0.4
+pytest>=7.1.3
+mypy>=0.971
+
+-e .

setup.cfg

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+[metadata]
+license = MIT
+license_file = LICENSE 
+classifiers = 
+    Programming Language :: Python :: 3.8
+    Programming Language :: Python :: 3.9
+    Programming Language :: Python :: 3.10
+    Operating System :: OS Independent
+
+[options]
+install_requires =
+    ensure>=1.0.2
+    pandas>=2.0.2
+    numpy>=1.24.4
+    matplotlib>=3.7.5
+    statsmodels>=0.14.1
+    arch
+    
+python_requires = >=3.8
+
+[options.extras_require]
+testing = 
+    pytest>=7.1.3
+    mypy>=0.971
+    flake8>=5.0.4
+    tox>=3.25.1
+    black>=22.8.0
+
+[options.package_data]
+dynamicts=py.typed
+
+[flake8]
+max-line-length = 160
+exclude = __init__.py

setup.py

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+from setuptools import setup, find_packages
+from typing import List
+    
+with open("README.md", "r", encoding='utf-8') as f:
+    long_description = f.read()
+    
+__version__ = "0.1.9"
+REPO_NAME = "DynamicTS"
+PACKAGE_NAME = "dynamicts"
+ORGANISATION_USERNAME = "Chinar-Quantum-AI-Ltd"
+ORGANISATION_EMAIL = "[email protected]"
+
+
+setup(
+    name=PACKAGE_NAME,
+    version=__version__,
+    description='A library for time series analysis and preprocessing',
+    author=ORGANISATION_USERNAME,
+    author_email=ORGANISATION_EMAIL,
+    long_description=long_description,
+    long_description_content_type="text/markdown",
+    url=f"https://github.com/{ORGANISATION_USERNAME}/{REPO_NAME}",
+    project_urls={
+        "Issue Tracker": f"https://github.com/{ORGANISATION_USERNAME}/{REPO_NAME}/issues",
+    },
+    package_dir={"": "."},
+    packages=find_packages(include=["dynamicts"]),
+)

smoothing.py

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+"""
+smoothing.py
+Simple exponential smoothing and plotting for time series data.
+"""
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from statsmodels.tsa.holtwinters import SimpleExpSmoothing
+
+def simple_exponential_smoothing(series, smoothing_level=None, optimized=True):
+    """Apply Simple Exponential Smoothing to a pandas Series."""
+    model = SimpleExpSmoothing(series.dropna())
+    fit = model.fit(smoothing_level=smoothing_level, optimized=optimized)
+    return fit.fittedvalues, fit
+
+def plot_ses(series, smoothing_level=None, optimized=True, title='Simple Exponential Smoothing'):
+    """Plot original series and SES smoothed series."""
+    fitted_values, _ = simple_exponential_smoothing(series, smoothing_level, optimized)
+    plt.figure(figsize=(10,6))
+    plt.plot(series, label='Original', color='blue')
+    plt.plot(fitted_values, label='SES Smoothed', color='green')
+    plt.title(title)
+    plt.legend(loc='best')
+    plt.show()

tox.ini

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+# Define which python envirnments tox should test against
+[tox]
+envlist = py38, py39, py310
+isolated_build = True
+
+# Here we map Github Actions Python versions to tox environment
+[gh-actions]
+python = 
+    3.8: py38
+    3.9: py39
+    3.10: py310
+
+[testenv]
+deps = -rrequirements_dev.txt
+commands = 
+    # Runs flake8 to report syntax errors & undefined names
+    flake8 dynamicts --count --select=E9,F63,F7,F82 --show-source --statistics
+    # Runs flake8 again to report all issues but exit with 0 (won’t fail build)
+    flake8 dynamicts --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+    # Runs static type checking with mypy on dynamicts package
+    ; mypy dynamicts
+    # Runs unit tests in verbose mode using pytest.
+    pytest -v tests/unit
+    pytest -v tests/integration