Practical Time Series Analysis Workshop
Dr. Imri Aharoni, DSRC Data scientist
19.10.2025 Sunday 09:00 - 16:00
22.10.2025 Wednesday 09:00 - 16:00
University of Haifa, Carmel campus
Building - TBD
Room - TBD
Prerequisites
- Basic knowledge of statistics (mean, variance, correlation)
- Prior experience with Python (pandas, numpy, matplotlib)
Suggested preparation
- IBM "Python for Data Science"
- Campus IL "Self.py" (Hebrew)
Reading Material
- IBM "Python for Data Science"
- Time Series Analysis with Python Cookbook – Tarek A. Atwan
- Machine Learning for Time-Series – Ben Auffarth
- Modern Statistics with Python – Ron Kenett et al.
Abstract
This hands-on workshop introduces participants to the core methods and modern techniques in time series analysis. Spanning from classical decomposition and forecasting to machine learning and deep learning models, the workshop integrates theory with practical coding exercises in Python.
Learning Objectives:
- Understand and decompose time series into basic components
- Test stationarity and transform non-stationary data
- Build classical models like: ARIMA, SARIMA
- Explore multivariate dependencies using VAR and ARIMAX
- Apply classic machine learning models for forecasting and anomaly detection
- Introduction to deep learning architectures like RNNs, LSTMs, and GRUs for time series predictions
Structure:
- 2 intensive days of lectures, 9:00 - 16:00, and practical practice in class (12 hours total)
- Python-based home assignments (12 hours)
- Group coding session (3 hours)
Target Audience
Graduate students, researchers, and professionals in any discipline using time-related data, seeking to enhance their analytical and forecasting capabilities
Dr. Imri Aharoni, DSRC Data scientist
19.10.2025 Sunday 09:00 - 16:00
22.10.2025 Wednesday 09:00 - 16:00
University of Haifa, Carmel campus
Building - TBD
Room - TBD
Prerequisites
- Basic knowledge of statistics (mean, variance, correlation)
- Prior experience with Python (pandas, numpy, matplotlib)
Suggested preparation
- IBM "Python for Data Science"
- Campus IL "Self.py" (Hebrew)
Reading Material
- IBM "Python for Data Science"
- Time Series Analysis with Python Cookbook – Tarek A. Atwan
- Machine Learning for Time-Series – Ben Auffarth
- Modern Statistics with Python – Ron Kenett et al.
Abstract
This hands-on workshop introduces participants to the core methods and modern techniques in time series analysis. Spanning from classical decomposition and forecasting to machine learning and deep learning models, the workshop integrates theory with practical coding exercises in Python.
Learning Objectives:
- Understand and decompose time series into basic components
- Test stationarity and transform non-stationary data
- Build classical models like: ARIMA, SARIMA
- Explore multivariate dependencies using VAR and ARIMAX
- Apply classic machine learning models for forecasting and anomaly detection
- Introduction to deep learning architectures like RNNs, LSTMs, and GRUs for time series predictions
Structure:
- 2 intensive days of lectures, 9:00 - 16:00, and practical practice in class (12 hours total)
- Python-based home assignments (12 hours)
- Group coding session (3 hours)
Target Audience
Graduate students, researchers, and professionals in any discipline using time-related data, seeking to enhance their analytical and forecasting capabilities
