Description
Time to take Time Series seriously!
From inventory to website visitors, resource planning to financial data, time-series data is all around us. Knowing what comes next is key to success in this dynamically changing world. And for that we need reliable forecasting models. While complex & deep models may be good at forecasting, they typically give us little insight about the underlying patterns in our data.
In this tutorial, we'll cover relatively simple yet powerful approaches for time series analysis and seasonality modeling with Pandas.
At the end of this session, you will be familiar with the fundamentals of time series analysis, how to decompose time series into trend, seasonality and error component, and how to use our insights to create simple but powerful models for forecasting.