Comprehensive Time Series Analysis and Forecasting Project Comprehensive Time Series Analysis and Forecasting Project

Overview

Developed a robust time series analysis and forecasting pipeline using Python, incorporating multiple advanced models including SARIMA, Prophet, XGBoost, LSTM, and Transformer. Implemented data preprocessing techniques such as handling missing values, resampling, and feature engineering to enhance model performance.

Highlights

1. 01

   Utilized concurrent processing with ThreadPoolExecutor to efficiently handle multiple SKUs, optimizing computational resources and reducing execution time.

2. 02

   Integrated advanced visualization techniques using Matplotlib and Plotly to create interactive and informative charts for trend analysis and forecast comparison.

3. 03

   Implemented inventory optimization algorithms, including EOQ and safety stock calculations, considering sustainability factors like CO2 emissions.

4. 04

   Leveraged GPU acceleration for deep learning models (LSTM, Transformer) using PyTorch and TensorFlow, significantly improving training speed.

5. 05

   Developed a custom Transformer model architecture for time series forecasting, showcasing adaptability to complex sequential data.

6. 06

   Implemented robust error handling and logging mechanisms to ensure reliable execution across large datasets.

7. 07

   Utilized Optuna for hyperparameter optimization, enhancing model performance through automated tuning.

8. 08

   Integrated geospatial analysis using Folium to visualize geographical patterns in sales and inventory distribution.

Stack

• Time Series Analysis
• Forecasting
• Python
• Machine Learning
• Deep Learning
• Data Preprocessing
• Visualization
• Inventory Optimization
• GPU Acceleration
• Hyperparameter Optimization
• Geospatial Analysis