diff --git a/README.md b/README.md
index 3c0d70051..498ae4318 100644
--- a/README.md
+++ b/README.md
@@ -237,11 +237,11 @@ To maximize its usefulness in real world contexts, `ydata-profiling` has a set o
 | Integration type | Description |
 |---|---|
 | [Other DataFrame libraries](https://docs.profiling.ydata.ai/latest/integrations/other_dataframe_libraries) | How to compute the profiling of data stored in libraries other than pandas |
-| [Great Expectations](https://ydata-profiling.ydata.ai/docs/master/pages/integrations/great_expectations.html) | Generating [Great Expectations](https://greatexpectations.io) expectations suites directly from a profiling report |
+| [Great Expectations](https://docs.profiling.ydata.ai/latest/integrations/great_expectations/) | Generating [Great Expectations](https://greatexpectations.io) expectations suites directly from a profiling report |
 | [Interactive applications](https://docs.profiling.ydata.ai/latest/integrations/interactive_applications) | Embedding profiling reports in [Streamlit](http://streamlit.io), [Dash](http://dash.plotly.com) or [Panel](https://panel.holoviz.org) applications |
 | [Pipelines](https://ydata-profiling.ydata.ai/docs/master/pages/integrations/pipelines.html) | Integration with DAG workflow execution tools like [Airflow](https://airflow.apache.org) or [Kedro](https://kedro.org) |
 | [Cloud services](https://ydata-profiling.ydata.ai/docs/master/pages/integrations/cloud_services.html) | Using `ydata-profiling` in hosted computation services like [Lambda](https://lambdalabs.com), [Google Cloud](https://github.com/GoogleCloudPlatform/analytics-componentized-patterns/blob/master/retail/propensity-model/bqml/bqml_kfp_retail_propensity_to_purchase.ipynb) or [Kaggle](https://www.kaggle.com/code) |
-| [IDEs](https://ydata-profiling.ydata.ai/docs/master/pages/integrations/ides.html) | Using `ydata-profiling` directly from integrated development environments such as [PyCharm](https://www.jetbrains.com/pycharm/) |
+| [IDEs](https://docs.profiling.ydata.ai/latest/integrations/ides/) | Using `ydata-profiling` directly from integrated development environments such as [PyCharm](https://www.jetbrains.com/pycharm/) |
 
 ## 🙋 Support
 Need help? Want to share a perspective? Report a bug? Ideas for collaborations? Reach out via the following channels:
diff --git a/src/ydata_profiling/config.py b/src/ydata_profiling/config.py
index 3fdcef09c..6d87d57df 100644
--- a/src/ydata_profiling/config.py
+++ b/src/ydata_profiling/config.py
@@ -112,6 +112,8 @@ class TimeseriesVars(BaseModel):
     lags: List[int] = [1, 7, 12, 24, 30]
     significance: float = 0.05
     pacf_acf_lag: int = 100
+    autolag: Optional[str] = "AIC"
+    maxlag: Optional[int] = None
 
 
 class Univariate(BaseModel):
diff --git a/src/ydata_profiling/model/pandas/describe_timeseries_pandas.py b/src/ydata_profiling/model/pandas/describe_timeseries_pandas.py
index 5ffe99a9f..7db4d56f3 100644
--- a/src/ydata_profiling/model/pandas/describe_timeseries_pandas.py
+++ b/src/ydata_profiling/model/pandas/describe_timeseries_pandas.py
@@ -16,12 +16,15 @@
 
 
 def stationarity_test(config: Settings, series: pd.Series) -> Tuple[bool, float]:
-    significance_threshold = config.vars.timeseries.significance
-
     # make sure the data has no missing values
-    adfuller_test = adfuller(series.dropna())
+    adfuller_test = adfuller(
+        series.dropna(),
+        autolag=config.vars.timeseries.autolag,
+        maxlag=config.vars.timeseries.maxlag,
+    )
     p_value = adfuller_test[1]
 
+    significance_threshold = config.vars.timeseries.significance
     return p_value < significance_threshold, p_value
 
 
diff --git a/src/ydata_profiling/model/typeset.py b/src/ydata_profiling/model/typeset.py
index 65f0cd2d6..a8e5b9f2e 100644
--- a/src/ydata_profiling/model/typeset.py
+++ b/src/ydata_profiling/model/typeset.py
@@ -122,7 +122,7 @@ def get_relations() -> Sequence[TypeRelation]:
         @series_handle_nulls
         def contains_op(series: pd.Series, state: dict) -> bool:
             return (
-                not pdt.is_categorical_dtype(series)
+                not isinstance(series.dtype, pd.CategoricalDtype)
                 and pdt.is_string_dtype(series)
                 and series_is_string(series, state)
             )
@@ -205,9 +205,9 @@ def get_relations() -> Sequence[TypeRelation]:
         @series_not_empty
         @series_handle_nulls
         def contains_op(series: pd.Series, state: dict) -> bool:
-            is_valid_dtype = pdt.is_categorical_dtype(series) and not pdt.is_bool_dtype(
-                series
-            )
+            is_valid_dtype = isinstance(
+                series.dtype, pd.CategoricalDtype
+            ) and not pdt.is_bool_dtype(series)
             if is_valid_dtype:
                 return True
             return False
diff --git a/src/ydata_profiling/model/typeset_relations.py b/src/ydata_profiling/model/typeset_relations.py
index 1a32526b0..beac15b51 100644
--- a/src/ydata_profiling/model/typeset_relations.py
+++ b/src/ydata_profiling/model/typeset_relations.py
@@ -31,7 +31,7 @@ def string_is_bool(series: pd.Series, state: dict, k: Dict[str, bool]) -> bool:
     def tester(s: pd.Series, state: dict) -> bool:
         return s.str.lower().isin(k.keys()).all()
 
-    if pdt.is_categorical_dtype(series):
+    if isinstance(series.dtype, pd.CategoricalDtype):
         return False
 
     return tester(series, state)