Have you ever struggled to identify state changes in time series data? Maybe you’re working with a dataset that tracks signal strength between a transmitter and a receiver, and you want to detect when the signal starts or stops moving. This can be a challenging task, especially when dealing with noisy or complex data.
Recently, I came across a Reddit post from someone who was facing a similar problem. They had a time series of detection data that they wanted to analyze to identify when the signal was moving or stationary. They had already trended the data to assign negative values to stationary points and positive values to moving points, but they needed help figuring out how to detect state changes.
The solution involved creating a rolling window that samples 20 points of data at a time, moving forward through the data one point at a time. As the window moves, it tracks the up or down trend of points. If the trend changes from negative to positive, it indicates a state change from stationary to moving. Conversely, if the trend changes from positive to negative, it indicates a state change from moving to stationary.
This approach can be applied to various types of time series data, such as sensor readings, financial data, or website traffic. By detecting state changes, you can gain insights into patterns and trends that might be hidden in the data.
So, the next time you’re working with time series data, don’t be afraid to get creative with your analysis. With the right approach, you can uncover valuable insights that might have gone unnoticed otherwise.