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The Kalman Filter is a state-space model that estimates the state of a dynamic system based on a series of noisy observations.

It uses a feedback mechanism called the Kalman gain to adjust the weight given to predicted and observed values based on their relative uncertainties.

We use Darts, which is a powerful and user-friendly Python library for time series forecasting that offers a range of models, tools, and utilities.

It provides a simple interface for loading, preprocessing, and modeling time series data, making it an ideal choice for beginners and experts alike.

Although not magical, they are a set of mathematical equations that help estimate the true state of dynamic systems (like moving objects) by factoring in measurement uncertainties and noise.

For a very noisy time series, like stock prices, the Kalman Filter can be used to smooth out the noise, get closer to the “true” price, and make predictions.

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