Update README.md

Author: wwrechard

README.md

@@ -6,6 +6,11 @@ Welcome to [pydlm](https://pydlm.github.io/), a flexible time series modeling li
 
 Updates in the github version
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+* Plan to refactor the `dlm` class. Align with the goal to separate model and data, I'm going to refactor the `dlm` class such that
+  1. The main `dlm` class will only contain model build information and is supposed to be 'const' after construction.
+  2. Time series data will be passed in as an argument to the `fit` or `forwardFilter` and the fitted result will be returned as well as the model status.
+  3. Model status can also be passed into `fit` and `forwardFilter` as a prior.
+  The goal is to make the `dlm` class state-independent, so that the class is thread-safe and can be shared by multiple threads for parallel processing. While in progress, all the old class behavior will be kept.
 * Deprecate the `data` argument in the [`autoReg`](https://pydlm.github.io/class_ref.html#autoreg) component. Now this component gets raw data directly from the main `dlm` class for constructing its regression features. User don't need to input when instantiate `autoReg`. This refactoring is for two purposes:
   1. The prediction function is largely simplified as `autoReg` can now handle the forecasting by itself. 
   2. This is towards the effort to separate `data` and `model` inside `dlm`. After this change, the internal dlm-builder only holds the model info (e.g., model status and structure) and does not depend on any time series data (except features in `dynamic`). As the next step, we will allow `dlm` to export the lightweighted dlm-builder and also recreated from the dlm-builder with new data, so that streaming/batch users won't suffer from the incremental data size.