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Matthew Kolakowski Learning Journey with Articles on Time Series Analysis, Machine Learning, Python Programming, and Systems Engineering.

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.github/workflows
.github/workflows
 
 
AdventofCode2024
AdventofCode2024
 
 
examples
examples
 
 
nbs/docs/models
nbs/docs/models
 
 
A Fuzzy K-Nearest Neighbor Algorithm.pdf
A Fuzzy K-Nearest Neighbor Algorithm.pdf
 
 
A Time Series Analysis-Based Forecasting Framework for the Indian Healthcare Sector.pdf
A Time Series Analysis-Based Forecasting Framework for the Indian Healthcare Sector.pdf
 
 
ARXIV_Conformal_Prediction.ipynb
ARXIV_Conformal_Prediction.ipynb
 
 
ARXIV_Conformal_Prediction_Revised.ipynb
ARXIV_Conformal_Prediction_Revised.ipynb
 
 
ARXIV_Time_Series.ipynb
ARXIV_Time_Series.ipynb
 
 
ARXIV_Time_Series_Final.ipynb
ARXIV_Time_Series_Final.ipynb
 
 
Bias, Fairness, and Accountability with AI and ML Algorithms.pdf
Bias, Fairness, and Accountability with AI and ML Algorithms.pdf
 
 
Cocktails.ipynb
Cocktails.ipynb
 
 
Curl.c
Curl.c
 
 
Darts Time Series.pdf
Darts Time Series.pdf
 
 
Designing Inherently Interpretable Machine Learning Models.pdf
Designing Inherently Interpretable Machine Learning Models.pdf
 
 
Discriminatory Analysis. Nonparametric Discrimination- Consistency Properties.pdf
Discriminatory Analysis. Nonparametric Discrimination- Consistency Properties.pdf
 
 
Forecasting Literature Review.pdf
Forecasting Literature Review.pdf
 
 
Fuzzy.pdf
Fuzzy.pdf
 
 
Hierarchical Probabilistic Forecasting of Electricity Demand With Smart Meter Data.pdf
Hierarchical Probabilistic Forecasting of Electricity Demand With Smart Meter Data.pdf
 
 
Hierarchical Time Series Forecasting in Emergency Medical Services.pdf
Hierarchical Time Series Forecasting in Emergency Medical Services.pdf
 
 
Improving Out of Sample Forecasts With Forecast Reconciliation and Clustering.pdf
Improving Out of Sample Forecasts With Forecast Reconciliation and Clustering.pdf
 
 
Low Code AutoML Augmented Data Pipelines.pdf
Low Code AutoML Augmented Data Pipelines.pdf
 
 
MAPIE and CREPES.pdf
MAPIE and CREPES.pdf
 
 
MAPIE- Open Source Library for Uncertainty Quantification.pdf
MAPIE- Open Source Library for Uncertainty Quantification.pdf
 
 
Model Selection In Reconciling Hierarchical Time Series.pdf
Model Selection In Reconciling Hierarchical Time Series.pdf
 
 
Network.py
Network.py
 
 
Pipeline Toolkit for Medical Time Series.pdf
Pipeline Toolkit for Medical Time Series.pdf
 
 
Plotly.py
Plotly.py
 
 
PyCaret Global Mean Temperature Prediction.pdf
PyCaret Global Mean Temperature Prediction.pdf
 
 
PyCaret Prediction of Fraction Unbound in Human Plasma.pdf
PyCaret Prediction of Fraction Unbound in Human Plasma.pdf
 
 
README.md
README.md
 
 
Ridge Regression- Applications to Nonorthogonal Problems.pdf
Ridge Regression- Applications to Nonorthogonal Problems.pdf
 
 
SVG.py
SVG.py
 
 
Selecting the Right Time Series Methodology.pdf
Selecting the Right Time Series Methodology.pdf
 
 
Simple_Moving_Average.ipynb
Simple_Moving_Average.ipynb
 
 
Testing_for_a_Unit_Root_in_a_Time_Series.pdf
Testing_for_a_Unit_Root_in_a_Time_Series.pdf
 
 
Tetris.py
Tetris.py
 
 
The Data Science Design Manual.pdf
The Data Science Design Manual.pdf
 
 
The Harm of Class Imbalance Corections for Risk Prediction.pdf
The Harm of Class Imbalance Corections for Risk Prediction.pdf
 
 
Why Backtesting Matters and How to Do It Right _ by Davide Burba _ Jul, 2023 _ Towards Data Science.pdf
Why Backtesting Matters and How to Do It Right _ by Davide Burba _ Jul, 2023 _ Towards Data Science.pdf
 
 
YOLO_NAS_POSE_Inference_Quick_Start.ipynb
YOLO_NAS_POSE_Inference_Quick_Start.ipynb
 
 
advanced-pyspark-for-exploratory-data-analysis.ipynb
advanced-pyspark-for-exploratory-data-analysis.ipynb
 
 
arxiv_time_series_data.csv
arxiv_time_series_data.csv
 
 
conformal_prediction_data.csv
conformal_prediction_data.csv
 
 
conformal_prediction_data_filtered_final.csv
conformal_prediction_data_filtered_final.csv
 
 
gpt_online.ipynb
gpt_online.ipynb
 
 
network_data.csv
network_data.csv
 
 
pong.py
pong.py
 
 
time_series_filtered.csv
time_series_filtered.csv
 
 
time_series_filtered.xlsx
time_series_filtered.xlsx
 
 
traffic_data.csv
traffic_data.csv
 
 

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LinkedIn Learning Journey 🎯

License: MIT Maintenance

Overview 📚

Welcome to my professional journey! While I have neglected this repository the past several months, I am back to creating a diverse information ecosystem on the four topics below.

Focus Areas 🎯

1. Time Series Forecasting

  • Advanced forecasting methodologies
  • Statistical analysis and modeling
  • Machine learning applications in time series
  • Real-world implementation cases

2. Open-Source Software Packages

  • Contributing to community projects
  • Package development and maintenance
  • Documentation and best practices
  • Code quality and testing frameworks

3. Peer-Reviewed Research Articles

  • Literature review and analysis
  • Implementation of research findings
  • Critical evaluation of methodologies
  • Research-based improvements

4. Systems Engineering Knowledge

  • Architecture design patterns
  • System integration strategies
  • Performance optimization
  • Scalability considerations

Progress Tracking 📊

Check individual directories for specific documentation and code samples.

Learning Resources 📖

  • Industry-standard documentation
  • Academic papers and publications
  • Online courses and certifications
  • Professional workshops and seminars

Contributing 🤝

While this is a personal learning journey, I welcome discussions and collaborations! Feel free to:

  • Share relevant resources
  • Suggest improvements
  • Connect for knowledge exchange

Contact 📫

Last Updated: December 2024

About

Matthew Kolakowski Learning Journey with Articles on Time Series Analysis, Machine Learning, Python Programming, and Systems Engineering.

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machine-learning systems-engineering time-series-analysis pythonprogramming

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