Comprehensive signal processing techniques for time series data analysis with practical applications in real-world datasets
This project implements advanced signal processing techniques and time series analysis methods on historical data. We explore frequency domain analysis, filtering techniques, and statistical signal processing to extract meaningful insights from temporal data.
- Frequency Domain Analysis - FFT and spectral analysis
- Digital Filter Design - Low-pass, high-pass, and bandpass filters
- Time Series Decomposition - Trend, seasonality, and noise separation
- Statistical Signal Processing - Autocorrelation and cross-correlation
- Real-world Data Application - Historical dataset analysis
# Frequency domain analysis
# Digital filtering techniques
# Time series decomposition
# Statistical signal analysis
# Noise reduction algorithms- Data Preprocessing - Cleaning and normalization
- Exploratory Analysis - Time domain visualization
- Frequency Analysis - Spectral content examination
- Filter Design - Custom filter implementation
- Feature Extraction - Signal characteristics identification
- Fourier Transform analysis for frequency content
- Digital filter design (FIR/IIR filters)
- Window functions and spectral leakage
- Signal-to-noise ratio optimization
- Aliasing and sampling theory
- Autocorrelation function analysis
- Power spectral density estimation
- Trend analysis and detrending
- Seasonal decomposition techniques
- Statistical stationarity testing
- Dataset: BAY001 Historical Data (time series)
- Domain: Historical trend analysis
- Frequency: Temporal measurements
- Challenges: Noise reduction, pattern extraction
- Temporal patterns identification
- Frequency content analysis
- Noise characterization
- Statistical properties evaluation
- Language: Python 3.8+
- Signal Processing: scipy.signal, numpy.fft
- Data Analysis: pandas, numpy
- Visualization: matplotlib, seaborn
- Time Series: statsmodels, scipy
- Environment: Jupyter Notebook
Signal-Processing/
├── Projet_SignalProcessing_JulesOdje.ipynb # Main analysis notebook
├── Projet_SignalProcessing_JulesOdje.pdf # Technical report
├── BAY001_Historische_Daten.csv # Historical dataset
├── requirements.txt # Dependencies
├── LICENSE # MIT License
├── README.md # This file
└── results/ # Analysis outputs
├── spectral_analysis.png # Frequency domain plots
├── filtered_signals.png # Filter application results
└── time_series_decomposition.png # Component analysis
- Finance - Market signal analysis and trend detection
- Engineering - System response analysis
- Telecommunications - Signal quality assessment
- Scientific Research - Experimental data analysis
- Noise reduction and signal enhancement
- Trend extraction from noisy data
- Periodic pattern identification
- Anomaly detection in time series
This work demonstrates expertise in:
- Digital Signal Processing - Advanced filtering and analysis
- Mathematical Analysis - Fourier methods and spectral theory
- Statistical Methods - Time series statistical analysis
- Practical Implementation - Real-world data application
Educational Background:
- Master's Statistics - Advanced time series methods
- Master's MIASHS/AI - Mathematical signal processing
- Strong foundation in mathematical analysis and statistics
- Frequency content characterization
- Optimal filter design parameters
- Trend and seasonality extraction
- Noise reduction effectiveness
- Custom filter design for specific data characteristics
- Statistical validation of signal processing methods
- Comprehensive spectral analysis framework
Jules Odje - Data Scientist | Aspiring PhD Researcher
📧 odjejulesgeraud@gmail.com
🔗 LinkedIn
🐙 GitHub
Expertise Areas: Signal Processing | Time Series Analysis | Mathematical Statistics
"Extracting meaningful patterns from temporal data through advanced signal processing"