Temperature Analysis Using EEMD Method with Implementation Code

Below is a comprehensive program for temperature analysis using the EEMD method that you can run directly. The code includes detailed calling procedures and analysis workflows to enhance your understanding. First, import essential libraries such as numpy for numerical computations and matplotlib for data visualization. The implementation requires proper installation of these dependencies through package managers like pip. Next, define a data loading function that reads temperature datasets from CSV or text files, followed by preprocessing routines to handle missing values using interpolation methods and remove outliers through statistical filtering (e.g., Z-score thresholding). Then, implement the EEMD algorithm which adds white noise to the original signal multiple times and performs Empirical Mode Decomposition (EMD) on each noisy version. The key function involves ensemble averaging of Intrinsic Mode Functions (IMFs) to eliminate noise effects, decomposing temperature data into multiple IMFs representing different frequency components and a residual trend. After decomposition, select specific IMFs for frequency-domain analysis and visualization using matplotlib's plotting capabilities. Each IMF can be plotted separately to examine distinct oscillatory patterns in the temperature data, with frequency characteristics revealed through Hilbert-Huang transform implementation. Finally, perform statistical analysis on the IMFs including mean value calculation, standard deviation quantification, and correlation analysis between different modes. These metrics provide insights into temperature variability patterns and help identify dominant cyclical components. This complete program enables thorough understanding and analysis of temperature data, offering robust support for research and decision-making processes through systematic signal processing techniques.