Are you ready to delve into the world of Time Series Analysis and Data Forecasting? This practical seminar is designed for data enthusiasts, analysts, and professionals who seek to enhance their data analysis toolkit with Python.
In this hands-on session, you will:
- Understand Time Series Basics: Learn the fundamental concepts of time series data, including trends, seasonality, and noise.
- Data Preprocessing: Discover techniques to prepare and clean your time series data for accurate analysis.
- Visualization: Utilize Python libraries like Matplotlib and Seaborn to visualize time series data effectively.
- Decomposition: Break down time series data into its components to understand underlying patterns.
- Modeling and Forecasting: Implement and compare various forecasting models such as ARIMA, Exponential Smoothing, and Prophet.
- Validation and Evaluation: Assess the performance of your models using metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).
- Real-World Applications: Apply your skills to real-world datasets and case studies to understand the practical implications of time series forecasting.
