Short-Time Fourier Transform Implementation for Time Series Analysis

This source code provides an efficient implementation of Short-Time Fourier Transform (STFT) for time series analysis. The program offers excellent convenience for users to rapidly process and analyze time series data through a streamlined workflow. When utilizing this program, users simply need to input their time-series data, and the algorithm automatically performs the necessary computations including windowing, Fourier transformation, and spectral analysis. The implementation employs a sliding window approach where the input signal is divided into overlapping segments, each multiplied by a window function (typically Hanning or Hamming) before applying Fast Fourier Transform (FFT). Key functions include parameter configuration for window size, overlap percentage, and frequency resolution adjustment. The output generates time-frequency representations (spectrograms) that reveal how the frequency content of the signal evolves over time. Furthermore, the program demonstrates high extensibility, allowing users to modify core parameters and customize processing logic according to specific requirements. This flexibility enables implementation of personalized features such as custom window functions, advanced filtering techniques, or alternative time-frequency analysis methods. The modular architecture supports easy integration of additional preprocessing steps or post-processing visualization components. In summary, this robust tool effectively serves various time series processing and analysis scenarios, particularly suitable for applications in signal processing, vibration analysis, audio processing, and non-stationary signal investigation where time-varying frequency characteristics need examination.