Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to Time Series Analysis
- Overview of time series data.
- Key components of time series: trend, seasonality, and noise.
- Configuring Google Colab for time series analysis.
Exploratory Data Analysis for Time Series
- Visualizing time series data.
- Decomposing time series components.
- Identifying seasonality and trends.
ARIMA Models for Time Series Forecasting
- Understanding ARIMA (AutoRegressive Integrated Moving Average).
- Selecting parameters for ARIMA models.
- Implementing ARIMA models in Python.
Introduction to Prophet for Time Series Forecasting
- Overview of Prophet for time series forecasting.
- Implementing Prophet models in Google Colab.
- Managing holidays and special events in forecasting.
Advanced Forecasting Techniques
- Addressing missing data in time series.
- Multivariate time series forecasting.
- Customizing forecasts with external regressors.
Evaluating and Fine-tuning Forecast Models
- Performance metrics for time series forecasting.
- Fine-tuning ARIMA and Prophet models.
- Cross-validation and backtesting.
Real-world Applications of Time Series Analysis
- Case studies of time series forecasting.
- Practical exercises with real-world datasets.
- Next steps for time series analysis in Python.
Summary and Next Steps
Requirements
- Intermediate proficiency in Python programming.
- Understanding of basic statistics and data analysis techniques.
Audience
- Data analysts.
- Data scientists.
- Professionals dealing with time series data.
21 Hours
Testimonials (2)
Doing Exercise
Joe Pang - Lands Department, Hong Kong
Course - QGIS for Geographic Information System
Hands-on examples allowed us to get an actual feel for how the program works. Good explanations and integration of theoretical concepts and how they relate to practical applications.
