Decomposition of time series data into trend, seasonal, and residual components Trend analysis and forecasting using linear regression Time series forecasting using ARIMA, SARIMA, and exponential smoothing models
Seasonal patterns and seasonal ARIMA models (SARIMA) Exponential smoothing models Holt-Winters method for seasonal data
Stationarity and non-stationarity Autocorrelation and partial autocorrelation AR, MA, and ARMA models
Overview of time series data and its characteristics Applications of time series analysis in various domains Time series data visualization and exploration
You will get an auto-generated certificate after the completion of the course. If you need a well designed course, write us at info@ambilio.com.
Data scientists, AI Engineer, AI Scientists, etc. roles can be explored after taking this course.
The course has beginner-level difficulty.
Basic understanding of AI, machine learning, Deep learning and related fields along with python, and mathematics is required to attend this course.
This course has different modes of delivery. It can be taken in person from a center over the weeks, or it can be taken online as well based on your own convenience.
This course is suitable for beginners as well as professionals working in different domains.