This repository contains solutions to the assignments from the Experimental Data Processing course. The course focuses on practical approaches to data processing for control and forecasting, with an emphasis on uncovering hidden features and regularities in dynamical processes using experimental data.
The course introduces students to useful methods for processing experimental data, with applications in navigation, solar physics, geomagnetism, and space weather research.
- Data Processing for Control and Forecasting: Techniques for predicting system behavior based on historical data.
- State-Space Models: Statistical approaches for modeling complex processes.
- Parameter Identification: Methods for extracting key parameters from experimental data to reveal hidden regularities.
- Applications: Practical examples in navigation, solar physics, geomagnetism, space weather, and biomedical research.
/code: Contains solutions to each assignment, Python code./reports: Reports on completed tasks./datasets: Experimental datasets used for various tasks in the assignments./final_project: Contains description, solution and presentation on final group project of the course.
Clone the repository to explore the solutions and follow along with the assignments:
git clone https://github.com/LizaP9/Experimental-Data-Processing.gitNote: it is necessary to replace the paths to the data files before running the code.