Fix typos in documentation

docs/source/forecasting.md

@@ -1,6 +1,6 @@
 # Time Series Forecasting
 
-Time series forecasting models (TSFMs) predict future values of a series from its observed history.
+Time series foundation models (TSFMs) predict future values of a series from its observed history.
 While many forecasting foundation models originally focused on predicting a single variable from its own history (**univariate forecasting**), newer models such as [Chronos-2](https://arxiv.org/abs/2510.15821) and [TimesFM-3](https://research.google/blog/timesfm-3-a-zero-shot-foundation-model-for-multivariate-forecasting) extend this paradigm to multiple targets (**multivariate forecasting**) and **covariates**: additional variables that inform the forecast (*e.g.*, historical temperature or planned promotions).
 These capabilities allow models to draw on relationships between targets, past observations of external factors, and known future information.
 
@@ -39,7 +39,7 @@ We begin with a single target, the daily sales of ice cream, observed for five d
   </tr>
 </table>
 
-A time series is a {py:class}`~sdm.tensor.TableTensor` in which each row is one time step, sorted in time, and each column is one variable:
+We represent a time series as a {py:class}`~sdm.tensor.TableTensor` with one row per time step, sorted in time, and one column per variable:
 
 ```python
 import sdm
@@ -51,7 +51,7 @@ history = sdm.TableTensor.from_columns(
 )
 ```
 
-We can forecast with any time series foundation model in [`sdm.models`](api/models).
+We can forecast with any time series forecasting model in [`sdm.models`](api/models).
 Here, we use {py:class}`~sdm.models.TimesFM3`:
 
 ```python
@@ -65,7 +65,7 @@ out = model(
 
 Note that `x_context` and `x_query` carry the covariates.
 Since we have none yet, both tables hold zero columns.
-However, we still need to pass them to {py:meth}`~sdm.models.ICLModel.forward`, in particular because the number of rows in `x_query` define the **forecast horizon**: here, we request predictions for the next three time steps.
+However, we still need to pass them to {py:meth}`~sdm.models.ICLModel.forward`, in particular because the number of rows in `x_query` defines the **forecast horizon**: here, we request predictions for the next three time steps.
 
 The forecast is returned as a {py:class}`~sdm.tensor.TableTensor` with one row per future step.
 Its output is model-specific, *e.g.*, {py:class}`~sdm.models.TimesFM3` outputs one column per target and quantile, from the 10th to the 90th percentile, named `"<target>__q<level>"`:
@@ -78,7 +78,7 @@ out["ice_cream__q90"].numerical  # upper end of an 80% prediction interval
 
 ## Multivariate Forecasting
 
-Models with {py:attr}`sdm.models.ICLModel.supports_multi_target` can forecast multiple targets jointly, allowing them to exploit dependencies between them.
+A model that {py:attr}`~sdm.models.ICLModel.supports_multi_target` can forecast multiple targets jointly, allowing them to exploit dependencies between them.
 
 In this case, we can simply add the daily sales of cold drinks as a second target:
 
@@ -112,8 +112,8 @@ out = model(
 ## Covariates
 
 Lastly, we add known future and past-only covariates to our model.
-For example, the measured **temperature** is a past covariate: its values are unavailable for the forecast horizon.
-Models with {py:attr}`sdm.models.ICLModel.supports_context_only_columns` can exploit such context-only columns.
+For example, the measured **temperature** is a past-only covariate: its values are unavailable for the forecast horizon.
+A model that {py:attr}`~sdm.models.ICLModel.supports_context_only_columns` can exploit such past-only covariates.
 In contrast, **promotion** is a known future covariate that indicates whether a promotion was or is scheduled for a given day.
 
 <table>
@@ -123,10 +123,8 @@ In contrast, **promotion** is a known future covariate that indicates whether a
     <th>ice cream</th><td>40</td><td>55</td><td>80</td><td>75</td><td>50</td><td>?</td><td>?</td><td>?</td>
   </tr><tr>
     <th>cold drinks</th><td>25</td><td>30</td><td>45</td><td>41</td><td>28</td><td>?</td><td>?</td><td>?</td>
-  </tr>
   </tr><tr>
     <th>temperature</th><td>21</td><td>24</td><td>28</td><td>27</td><td>22</td><td></td><td></td><td></td>
-  </tr>
   </tr><tr>
     <th>promotion</th><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td>
   </tr>
@@ -142,9 +140,9 @@ history = sdm.TableTensor.from_columns(
     },
     stypes={
         "ice_cream": "numerical",
-        "cold_drinks": "numerical"
-        "temperature": "numerical"
-        "promotion": "numerical"
+        "cold_drinks": "numerical",
+        "temperature": "numerical",
+        "promotion": "numerical",
     },
     device="cuda",
 )
@@ -171,7 +169,7 @@ In summary, the columns that are passed determine which covariates the model see
 
 As with any other {py:class}`~sdm.models.ICLModel`, the `num_estimators` argument controls how many ensemble members contribute to the forecast.
 Each member may receive a differently transformed view of the series, and their predictions are averaged into a single forecast.
-For example, the default recipe for {py:class}`~sdm.models.TimesFM3` alternatives between the original series and a sign-flipped version.
+For example, the default recipe for {py:class}`~sdm.models.TimesFM3` alternates between the original series and a sign-flipped version.
 Using two estimators includes both views:
 
 ```python
@@ -186,7 +184,7 @@ Each task itself can contain multiple targets and covariates.
 
 For example, store sales can be represented in two ways: as separate univariate tasks in a batch (forecasting each store independently) or as a multivariate task (allowing the model to use relationships between stores).
 
-To forecast a batch series in one call, stack them along a leading batch dimension.
+To forecast a batch of series in one call, stack them along a leading batch dimension.
 All series in a batch share the same horizon and number of context rows, and shorter series should be left-padded with `NaN`.
 Set `num_estimators` explicitly so that the leading dimension is interpreted as a batch of independent forecasting tasks rather than as ensemble members:
 
@@ -195,10 +193,10 @@ import torch
 
 out = model(
     x_context=torch.randn(64, 128, 0, device="cuda"),
-    x_context=torch.randn(64, 128, 2, device="cuda"),
+    y_context=torch.randn(64, 128, 2, device="cuda"),
     x_query=torch.randn(64, 32, 0, device="cuda"),
     num_estimators=1,
 )
 ```
 
-See [`examples/timeseries/quickstart.py`](https://github.com/NVIDIA/structured-data-models/blob/main/examples/timeseries/quickstart.py) for runnable examples on real-world datasets and tasks.
+See [`examples/timeseries/forecast.py`](https://github.com/NVIDIA/structured-data-models/blob/main/examples/timeseries/forecast.py) for runnable examples on real-world datasets and tasks.