Add tv intrecept to readme

README.md

@@ -59,6 +59,7 @@ Leverage our Bayesian MMM API to tailor your marketing strategies effectively. B
 - **Custom Priors and Likelihoods**: Tailor your model to your specific business needs by including domain knowledge via prior distributions.
 - **Adstock Transformation**: Optimize the carry-over effects in your marketing channels.
 - **Saturation Effects**: Understand the diminishing returns in media investments.
+- **Time-varying Intercept:** Capture time-varying baseline contributions in your model (using modern and efficient Gaussian processes approximation methods).
 - **Visualization and Model Diagnostics**: Get a comprehensive view of your model's performance and insights.
 - **Out-of-sample Predictions**: Forecast future marketing performance with credible intervals. Use this for simulations and scenario planning.
 - **Budget Optimization**: Allocate your marketing spend efficiently across various channels for maximum ROI.

docs/source/guide/mmm/comparison.md

@@ -11,7 +11,8 @@ Given the popularity of the Media Mix Modelling (MMM) approach, there are many p
 | Open source| ✅                  | ✅              | ✅                    | ✅       | ❌                 |
 | Model   | 🏗️ Build            | 🏗️ Build        |  🏗️ Build       | 🏗️ Build        | 🛒 Buy               |
 | Budget optimizer | ✅ | ✅ |   ✅        |   ❌        |    ✅                  |
-| Time-varying parameters | coming soon | ❌ | ❌ | ✅ | ✅ |
+| Time-varying intercept | ✅ | ❌ | ❌ | ✅ | ✅ |
+| Time-varying coefficients | coming soon | ❌ | ❌ | ✅ | ✅ |
 | Custom priors | ✅ | ✅ | ❌ | ❌ | ✅ |
 | Lift-test calibration | ✅  | ❌ | ✅ | ❌ | ✅ |
 | Out of sample predictions | ✅ | ✅ | ❌ | ✅ | ✅ |