Confidence intervals are a powerful statistical tool that can help to quantify
the uncertainty associated with estimates in regression tasks. This
presentation will focus on calculating and interpreting confidence intervals
for time series forecasting.

We will introduce the concept of confidence intervals and how they relate to
statistical significance. We will then discuss the different types of
confidence intervals, such as point estimates and interval estimates. We will
also cover the factors that can affect the width of confidence intervals, such
as sample size, variability, and confidence level. Finally, we will discuss
some common challenges and limitations of using confidence intervals in time
series forecasting; for example, the assumption of stationarity and the
potential for model misspecification. We will also provide some tips and best
practices for interpreting and communicating confidence intervals in the
context of time series forecasting.

Overall, this presentation will provide a comprehensive overview of confidence
intervals, explicitly focusing on their application in time series forecasting.
Attendees will gain a deeper understanding of calculating and interpreting
confidence intervals and the practical considerations involved in using them
for predictive modeling.