Understanding predictive modeling approaches

In this section, we’ll delve into predictive modeling approaches using two powerful Python libraries – statsmodels and prophet.

These libraries provide diverse tools to tackle time series forecasting, enabling you to make informed decisions and predictions based on your time series data.

Forecasting with statsmodels

statsmodels is a popular library in the Python ecosystem that offers a wide range of statistical tools, including time series analysis. For forecasting, it provides functionality for building ARIMA models. ARIMA models are a staple in time series analysis, allowing you to capture and model complex patterns within your data.

Building an ARIMA model with statsmodels involves selecting the appropriate order of differencing, autoregressive components, and moving average components to best represent the underlying patterns of the data. Once the model has been established, you can make forecasts and evaluate...