[WIP] Cleanup docstrings

docs/api/base.md

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+# Base
+
+::: chronos.base.BaseChronosPipeline
+    options:
+      show_source: true
+      heading_level: 2
+      show_root_heading: true
+      show_root_toc_entry: true
+      members:
+        - predict
+        - predict_quantiles
+        - predict_df
+        - predict_fev
+        - from_pretrained
+        - model_context_length
+        - model_prediction_length

docs/api/chronos-2.md

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+# Chronos
+
+::: chronos.chronos2.pipeline.Chronos2Pipeline
+    options:
+      show_source: true
+      heading_level: 2
+      show_root_heading: true
+      show_root_toc_entry: true
+      members: null
+      members:
+        - predict
+        - predict_quantiles
+        - predict_df
+        - predict_fev
+        - embed
+        - fit
+        - from_pretrained
+        - save_pretrained
+        - model_context_length
+        - model_prediction_length
+        - model_output_patch_size
+        - quantiles
+        - max_output_patches
+        - model
+        - forecast_type

docs/api/chronos-bolt.md

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+# Chronos
+
+::: chronos.chronos_bolt.ChronosBoltPipeline
+    options:
+      show_source: true
+      heading_level: 2
+      show_root_heading: true
+      show_root_toc_entry: true
+      members: null
+      members:
+        - predict
+        - predict_quantiles
+        - embed
+        - from_pretrained
+        - model_context_length
+        - model_prediction_length
+        - model
+        - forecast_type

docs/api/chronos.md

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+# Chronos
+
+::: chronos.chronos.ChronosPipeline
+    options:
+      show_source: true
+      heading_level: 2
+      show_root_heading: true
+      show_root_toc_entry: true
+      members:
+        - predict
+        - predict_quantiles
+        - embed
+        - from_pretrained
+        - model_context_length
+        - model_prediction_length
+        - tokenizer
+        - model
+        - forecast_type

docs/index.md

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+# chronos-forecasting
+
+
+## Introduction
+
+This package provides an interface to the Chronos family of **pretrained time series forecasting models**. The following model types are supported.
+
+- **Chronos-2**: Our latest model with significantly enhanced capabilities. It offers zero-shot support for univariate, multivariate, and covariate-informed forecasting tasks. Chronos-2 delivers state-of-the-art zero-shot performance across multiple benchmarks (including fev-bench and GIFT-Eval), with the largest improvements observed on tasks that include exogenous features. It also achieves a win rate of over 90% against Chronos-Bolt in head-to-head comparisons. To learn more about Chronos, check out the [technical report](https://arxiv.org/abs/2510.15821).
+- **Chronos-Bolt**: A patch-based variant of Chronos. It chunks the historical time series context into patches of multiple observations, which are then input into the encoder. The decoder then uses these representations to directly generate quantile forecasts across multiple future steps—a method known as direct multi-step forecasting. Chronos-Bolt models are up to 250 times faster and 20 times more memory-efficient than the original Chronos models of the same size. To learn more about Chronos-Bolt, check out this [blog post](https://aws.amazon.com/blogs/machine-learning/fast-and-accurate-zero-shot-forecasting-with-chronos-bolt-and-autogluon/).
+- **Chronos**: The original Chronos family which is based on language model architectures. A time series is transformed into a sequence of tokens via scaling and quantization, and a language model is trained on these tokens using the cross-entropy loss. Once trained, probabilistic forecasts are obtained by sampling multiple future trajectories given the historical context. To learn more about Chronos, check out the [publication](https://openreview.net/forum?id=gerNCVqqtR).
+
+### Available Models
+
+| Model ID                                                               | Parameters |
+| ---------------------------------------------------------------------- | ---------- |
+| [`amazon/chronos-2`](https://huggingface.co/amazon/chronos-2)   | 120M         |
+| [`amazon/chronos-bolt-tiny`](https://huggingface.co/amazon/chronos-bolt-tiny)   | 9M         |
+| [`amazon/chronos-bolt-mini`](https://huggingface.co/amazon/chronos-bolt-mini)   | 21M        |
+| [`amazon/chronos-bolt-small`](https://huggingface.co/amazon/chronos-bolt-small) | 48M        |
+| [`amazon/chronos-bolt-base`](https://huggingface.co/amazon/chronos-bolt-base)   | 205M       |
+| [`amazon/chronos-t5-tiny`](https://huggingface.co/amazon/chronos-t5-tiny)   | 8M         |
+| [`amazon/chronos-t5-mini`](https://huggingface.co/amazon/chronos-t5-mini)   | 20M        |
+| [`amazon/chronos-t5-small`](https://huggingface.co/amazon/chronos-t5-small) | 46M        |
+| [`amazon/chronos-t5-base`](https://huggingface.co/amazon/chronos-t5-base)   | 200M       |
+| [`amazon/chronos-t5-large`](https://huggingface.co/amazon/chronos-t5-large) | 710M       |
+
+
+
+
+## Installation
+
+```bash
+pip install chronos-forecasting
+```
+
+## Quickstart
+
+A minimal example showing how to perform forecasting using Chronos-2:
+
+```py
+import pandas as pd  # requires: pip install 'pandas[pyarrow]'
+from chronos import Chronos2Pipeline
+
+pipeline = Chronos2Pipeline.from_pretrained("amazon/chronos-2", device_map="cuda")
+
+# Load historical target values and past values of covariates
+context_df = pd.read_parquet("https://autogluon.s3.amazonaws.com/datasets/timeseries/electricity_price/train.parquet")
+
+# (Optional) Load future values of covariates
+test_df = pd.read_parquet("https://autogluon.s3.amazonaws.com/datasets/timeseries/electricity_price/test.parquet")
+future_df = test_df.drop(columns="target")
+
+# Generate predictions with covariates
+pred_df = pipeline.predict_df(
+    context_df,
+    future_df=future_df,
+    prediction_length=24,  # Number of steps to forecast
+    quantile_levels=[0.1, 0.5, 0.9],  # Quantile for probabilistic forecast
+    id_column="id",  # Column identifying different time series
+    timestamp_column="timestamp",  # Column with datetime information
+    target="target",  # Column(s) with time series values to predict
+)
+```
+
+We can now visualize the forecast:
+
+```py
+import matplotlib.pyplot as plt  # requires: pip install matplotlib
+
+ts_context = context_df.set_index("timestamp")["target"].tail(256)
+ts_pred = pred_df.set_index("timestamp")
+ts_ground_truth = test_df.set_index("timestamp")["target"]
+
+ts_context.plot(label="historical data", color="xkcd:azure", figsize=(12, 3))
+ts_ground_truth.plot(label="future data (ground truth)", color="xkcd:grass green")
+ts_pred["predictions"].plot(label="forecast", color="xkcd:violet")
+plt.fill_between(
+    ts_pred.index,
+    ts_pred["0.1"],
+    ts_pred["0.9"],
+    alpha=0.7,
+    label="prediction interval",
+    color="xkcd:light lavender",
+)
+plt.legend()
+```
+
+## Tutorials
+

mkdocs.yml

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+site_name: chronos-forecasting
+site_description: A library for the Chronos time series foundation models
+site_url: https://amazon-science.github.io/chronos-forecasting/
+repo_url: https://github.com/amazon-science/chronos-forecasting
+repo_name: amazon-science/chronos-forecasting
+
+theme:
+  name: material
+  palette:
+    primary: blue
+
+plugins:
+  - search
+  - mkdocstrings:
+      handlers:
+        python:
+          options:
+            docstring_style: numpy
+            show_root_heading: true
+            show_root_full_path: false
+            show_signature_annotations: true
+            show_category_heading: true
+            group_by_category: true
+            allow_inspection: true
+
+nav:
+  - Home: index.md
+  # - Tutorials:
+  #   - Quickstart: 
+  - API Reference:
+    - Base: api/base.md
+    - Chronos: api/chronos.md
+    - Chronos-Bolt: api/chronos-bolt.md
+    - Chronos-2: api/chronos-2.md
+
+markdown_extensions:
+  - pymdownx.highlight
+  - pymdownx.superfences
+  - attr_list
+  - md_in_html