Kalman Filters are perfect for time series forecasting in dynamic systems that require real-time tracking or estimation. They can especially come in handy when your forecasting relies on noisy or imprecise data, like inputs from sensors such as GPS or accelerometers. For instance, imagine you want to predict the future position of a moving object based on noisy sensor data. A Kalman Filter will help refine those readings and provide a more accurate forecast of the object's future state. Moreover, Kalman Filters are an excellent choice when you need to update your predictions as new data comes in. They can adapt seamlessly to the continuous inflow of data, adjusting your forecast in real-time while accounting for uncertainties in the measurements. However, there are times when using Kalman Filters may not be ideal:
- Static systems: If your system doesn't change over time (i.e., it's not dynamic), or if your measurements are already very accurate, then there's no need for the extra effort of filtering.
- Complex, nonlinear behavior: When your system exhibits nonlinear behavior, a Kalman Filter might not be sufficient. In such cases, consider exploring other options like [Particle Filters](https://en.wikipedia.org/wiki/Particle_filter), which are specifically designed to handle intricate situations.