Fix typos in Chronos-2 notebook (#2975)

* Fix notebook typos

Co-authored-by: Zhihao Dai <[email protected]>

* Update CHANGELOG for #2962

Co-authored-by: Zhihao Dai <[email protected]>

* Fix a CHANGELOG typo

Co-authored-by: Zhihao Dai <[email protected]>

* Fix CHANGELOG typo

Co-authored-by: Zhihao Dai <[email protected]>

Author: daidahao

CHANGELOG.md

@@ -17,6 +17,11 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
     - `plotting.use_darts_style`: Whether to apply Darts' custom matplotlib plotting style (default: True). Changes take effect immediately and apply to all subsequent plots.
   - The configuration API includes: `get_option()`, `set_option()`, `reset_option()`, `describe_option()`, and `option_context()` for temporary changes.
   - 🟠 The Darts plotting style is now not applied anymore by default. To activate it call `set_option('plotting.use_darts_style', True)` before plotting.
+- Added support for two new variants (trained and released by Amazon) to foundation model `Chronos2Model` besides the original 120M-parameter model. [#2962](https://github.com/unit8co/darts/pull/2962) by [Zhihao Dai](https://github.com/daidahao).
+  - [`"autogluon/chronos-2-small"`](https://huggingface.co/autogluon/chronos-2-small) : a smaller 28M parameter Chronos-2 model.
+  - [`"autogluon/chronos-2-synth"`](https://huggingface.co/autogluon/chronos-2-synth) : a 120M parameter Chronos-2 model trained on synthetic data only.
+  - To use either variant, simply set `hub_model_name` parameter to the desired model ID, e.g., `"autogluon/chronos-2-small"`.
+  - Both models can be used in the same way as the original Chronos-2 model.
 - `TorchForecastingModel` parameter `torch_metrics` now supports all input metric types from ``torchmetrics.MetricCollection``. Eg. now you can also pass a dictionary or sequence of metrics. [#2958](https://github.com/unit8co/darts/pull/2958) by [CorticallyAI](https://github.com/CorticallyAI).
 
 **Fixed**

examples/25-Chronos-2-examples.ipynb

@@ -97,7 +97,7 @@
    "id": "70d7e392",
    "metadata": {},
    "source": [
-    "Here, we will use the [Electricity Consumption Zurich Dataset](https://unit8co.github.io/darts/generated_api/darts.datasets.html#darts.datasets.ElectricityConsumptionZurichDataset), which records the electricity consumption of households & SMEs (`\"Value_NE5\"` column) and business & services (`\"Value_NE7\"`) in Zurich, Switzerland, along with weather covariates such as temperature (`\"T [°C]\"`) and humidity (`\"Hr [%Hr]\"`).\n",
+    "Here, we will use the [Electricity Consumption Zurich Dataset](https://unit8co.github.io/darts/generated_api/darts.datasets.datasets.html#darts.datasets.datasets.ElectricityConsumptionZurichDataset), which records the electricity consumption of households & SMEs (`\"Value_NE5\"` column) and business & services (`\"Value_NE7\"`) in Zurich, Switzerland, along with weather covariates such as temperature (`\"T [°C]\"`) and humidity (`\"Hr [%Hr]\"`).\n",
     "Values are recorded every 15 minutes between January 2015 and August 2022.\n",
     "\n",
     "<details>\n",
@@ -457,7 +457,7 @@
    "metadata": {},
    "source": [
     "## Probabilistic Forecasting\n",
-    "Here, we show how to perform probabilistic forecasting with Chronos-2 by using [`QuantileRegression`](https://unit8co.github.io/darts/generated_api/darts.utils.likelihood_models.sklearn.html#darts.utils.likelihood_models.sklearn.QuantileRegression) likelihood. The quantiles passed to `QuantileRegression` must be a subset of pre-trained quantiles supported by Chronos-2 (see \"Model Creation\" section above).\n",
+    "Here, we show how to perform probabilistic forecasting with Chronos-2 by using [`QuantileRegression`](https://unit8co.github.io/darts/generated_api/darts.utils.likelihood_models.torch.html#darts.utils.likelihood_models.torch.QuantileRegression) likelihood. The quantiles passed to `QuantileRegression` must be a subset of pre-trained quantiles supported by Chronos-2 (see \"Model Creation\" section above).\n",
     "\n",
     "Because sampling with large models like Chronos-2 can be computationally expensive, we here call `predict()` with `predict_likelihood_parameters=True` to obtain quantile estimates directly without sampling. However, if the forecast horizon is longer than `output_chunk_length` (i.e., auto-regressive forecasting is required), you must call `predict()` with a large enough `num_samples` value (e.g., 1000) to generate probabilistic forecasts via Monte Carlo sampling."
    ]