Today’s analysis involves looking at patterns—like trends or recurring behaviors—and figuring out how they unfold over different time periods. Imagine you’re tracking data points at different moments.
The first step is to organize and visually represent this data, often with timestamps showing when each piece was recorded. This helps us see the patterns more clearly. Next, we use various methods to break down the data, like splitting it into different parts or figuring out how one data point relates to another. We might also choose specific models (like mathematical formulas) to better understand what’s happening in the data.
To make sure our understanding is accurate, we use measures like Mean Squared Error or Mean Absolute Error to check how well our models predict or represent the changing patterns. Once we’ve built and confirmed our model, we can use it to make predictions about future values or to understand what might happen next in those patterns. It’s crucial to keep an eye on things over time, updating our analysis as we get more data. This way, we can be sure our understanding stays relevant and reflects any new developments. The specific tools we use for this depend on what we’re studying and what we want to find out. There are computer programs, like pandas and states models in Python, that make these analyses easier for us to do.