A Practical Approach to Timeseries Forecasting Using Python - RVT Models

The video tutorial explains time series decomposition, focusing on its components: level, trend, seasonality, and noise. It discusses how these components can be modeled using additive or multiplicative approaches. Additive models are linear with constant frequency and amplitude, while multiplicative models are non-linear with variable frequency and amplitude. The tutorial also highlights the importance of understanding these components for effective time series analysis and introduces automatic decomposition methods.

What are the main components of time series decomposition?

How does a trend in a time series manifest?

What role does seasonality play in time series analysis?

What is the difference between additive and multiplicative models in time series?

Why is it important to decompose a time series?