diff --git a/src/libs/Nixtla/Generated/Nixtla.INixtlaClient.V2AnomalyDetection.g.cs b/src/libs/Nixtla/Generated/Nixtla.INixtlaClient.V2AnomalyDetection.g.cs
index cee4e2c..57f7a13 100644
--- a/src/libs/Nixtla/Generated/Nixtla.INixtlaClient.V2AnomalyDetection.g.cs
+++ b/src/libs/Nixtla/Generated/Nixtla.INixtlaClient.V2AnomalyDetection.g.cs
@@ -6,7 +6,7 @@ public partial interface INixtlaClient
     {
         /// <summary>
         /// Foundational Time Series Model Multi Series Anomaly Detector<br/>
-        /// Based on the provided data, this endpoint detects the anomalies in the historical perdiod of multiple time series at once. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains a flag indicating if the date has an anomaly and also provides the prediction interval used to define if an observation is an anomaly.Get your token at https://nixtla.io/free-trial?utm_source=nixtla.io&amp;utm_campaign=/docs/api-reference.
+        /// Based on the provided data, this endpoint detects the anomalies in the historical perdiod of multiple time series at once. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains a flag indicating if the date has an anomaly and also provides the prediction interval used to define if an observation is an anomaly.Get your token at https://www.nixtla.io/book-a-free-trial?utm_source=nixtla.io&amp;utm_campaign=/docs/api-reference.
         /// </summary>
         /// <param name="request"></param>
         /// <param name="requestOptions">Per-request overrides such as headers, query parameters, timeout, retries, and response buffering.</param>
@@ -19,7 +19,7 @@ public partial interface INixtlaClient
             global::System.Threading.CancellationToken cancellationToken = default);
         /// <summary>
         /// Foundational Time Series Model Multi Series Anomaly Detector<br/>
-        /// Based on the provided data, this endpoint detects the anomalies in the historical perdiod of multiple time series at once. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains a flag indicating if the date has an anomaly and also provides the prediction interval used to define if an observation is an anomaly.Get your token at https://nixtla.io/free-trial?utm_source=nixtla.io&amp;utm_campaign=/docs/api-reference.
+        /// Based on the provided data, this endpoint detects the anomalies in the historical perdiod of multiple time series at once. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains a flag indicating if the date has an anomaly and also provides the prediction interval used to define if an observation is an anomaly.Get your token at https://www.nixtla.io/book-a-free-trial?utm_source=nixtla.io&amp;utm_campaign=/docs/api-reference.
         /// </summary>
         /// <param name="request"></param>
         /// <param name="requestOptions">Per-request overrides such as headers, query parameters, timeout, retries, and response buffering.</param>
@@ -32,7 +32,7 @@ public partial interface INixtlaClient
             global::System.Threading.CancellationToken cancellationToken = default);
         /// <summary>
         /// Foundational Time Series Model Multi Series Anomaly Detector<br/>
-        /// Based on the provided data, this endpoint detects the anomalies in the historical perdiod of multiple time series at once. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains a flag indicating if the date has an anomaly and also provides the prediction interval used to define if an observation is an anomaly.Get your token at https://nixtla.io/free-trial?utm_source=nixtla.io&amp;utm_campaign=/docs/api-reference.
+        /// Based on the provided data, this endpoint detects the anomalies in the historical perdiod of multiple time series at once. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains a flag indicating if the date has an anomaly and also provides the prediction interval used to define if an observation is an anomaly.Get your token at https://www.nixtla.io/book-a-free-trial?utm_source=nixtla.io&amp;utm_campaign=/docs/api-reference.
         /// </summary>
         /// <param name="series"></param>
         /// <param name="freq">
diff --git a/src/libs/Nixtla/Generated/Nixtla.INixtlaClient.V2Finetune.g.cs b/src/libs/Nixtla/Generated/Nixtla.INixtlaClient.V2Finetune.g.cs
index 88445f0..4778e77 100644
--- a/src/libs/Nixtla/Generated/Nixtla.INixtlaClient.V2Finetune.g.cs
+++ b/src/libs/Nixtla/Generated/Nixtla.INixtlaClient.V2Finetune.g.cs
@@ -6,7 +6,7 @@ public partial interface INixtlaClient
     {
         /// <summary>
         /// Foundational Time Series Model Multi Series Finetuning<br/>
-        /// Fine-tune the large time model to your data and save it for later use. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains the ID of the finetuned model, which you can provide in other endpoints to use that model to make the forecasts. Get your token at https://nixtla.io/free-trial?utm_source=nixtla.io&amp;utm_campaign=/docs/api-reference.
+        /// Fine-tune the large time model to your data and save it for later use. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains the ID of the finetuned model, which you can provide in other endpoints to use that model to make the forecasts. Get your token for private beta at https://www.nixtla.io/book-a-free-trial?utm_source=nixtla.io&amp;utm_campaign=/docs/api-reference.
         /// </summary>
         /// <param name="request"></param>
         /// <param name="requestOptions">Per-request overrides such as headers, query parameters, timeout, retries, and response buffering.</param>
@@ -19,7 +19,7 @@ public partial interface INixtlaClient
             global::System.Threading.CancellationToken cancellationToken = default);
         /// <summary>
         /// Foundational Time Series Model Multi Series Finetuning<br/>
-        /// Fine-tune the large time model to your data and save it for later use. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains the ID of the finetuned model, which you can provide in other endpoints to use that model to make the forecasts. Get your token at https://nixtla.io/free-trial?utm_source=nixtla.io&amp;utm_campaign=/docs/api-reference.
+        /// Fine-tune the large time model to your data and save it for later use. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains the ID of the finetuned model, which you can provide in other endpoints to use that model to make the forecasts. Get your token for private beta at https://www.nixtla.io/book-a-free-trial?utm_source=nixtla.io&amp;utm_campaign=/docs/api-reference.
         /// </summary>
         /// <param name="request"></param>
         /// <param name="requestOptions">Per-request overrides such as headers, query parameters, timeout, retries, and response buffering.</param>
@@ -32,7 +32,7 @@ public partial interface INixtlaClient
             global::System.Threading.CancellationToken cancellationToken = default);
         /// <summary>
         /// Foundational Time Series Model Multi Series Finetuning<br/>
-        /// Fine-tune the large time model to your data and save it for later use. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains the ID of the finetuned model, which you can provide in other endpoints to use that model to make the forecasts. Get your token at https://nixtla.io/free-trial?utm_source=nixtla.io&amp;utm_campaign=/docs/api-reference.
+        /// Fine-tune the large time model to your data and save it for later use. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains the ID of the finetuned model, which you can provide in other endpoints to use that model to make the forecasts. Get your token for private beta at https://www.nixtla.io/book-a-free-trial?utm_source=nixtla.io&amp;utm_campaign=/docs/api-reference.
         /// </summary>
         /// <param name="series"></param>
         /// <param name="freq">
diff --git a/src/libs/Nixtla/Generated/Nixtla.INixtlaClient.V2OnlineAnomalyDetection.g.cs b/src/libs/Nixtla/Generated/Nixtla.INixtlaClient.V2OnlineAnomalyDetection.g.cs
index a694c6f..7fce9da 100644
--- a/src/libs/Nixtla/Generated/Nixtla.INixtlaClient.V2OnlineAnomalyDetection.g.cs
+++ b/src/libs/Nixtla/Generated/Nixtla.INixtlaClient.V2OnlineAnomalyDetection.g.cs
@@ -6,7 +6,7 @@ public partial interface INixtlaClient
     {
         /// <summary>
         /// Foundational Time Series Model Online Multi Series Anomaly Detector<br/>
-        /// This endpoint performs online anomaly detection based on the provided data. It uses cross-validation for more robust detection of anomalies and it supports detection for univariate and multivariate scenarios. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains a flag indicating if the date has an anomaly, it provides the prediction interval used to define if an observation is an anomaly, and it reports the associated z-score for each point. Get your token at https://nixtla.io/free-trial?utm_source=nixtla.io&amp;utm_campaign=/docs/api-reference.
+        /// This endpoint performs online anomaly detection based on the provided data. It uses cross-validation for more robust detection of anomalies and it supports detection for univariate and multivariate scenarios. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains a flag indicating if the date has an anomaly, it provides the prediction interval used to define if an observation is an anomaly, and it reports the associated z-score for each point. Get your token for private beta at https://www.nixtla.io/book-a-free-trial?utm_source=nixtla.io&amp;utm_campaign=/docs/api-reference.
         /// </summary>
         /// <param name="request"></param>
         /// <param name="requestOptions">Per-request overrides such as headers, query parameters, timeout, retries, and response buffering.</param>
@@ -19,7 +19,7 @@ public partial interface INixtlaClient
             global::System.Threading.CancellationToken cancellationToken = default);
         /// <summary>
         /// Foundational Time Series Model Online Multi Series Anomaly Detector<br/>
-        /// This endpoint performs online anomaly detection based on the provided data. It uses cross-validation for more robust detection of anomalies and it supports detection for univariate and multivariate scenarios. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains a flag indicating if the date has an anomaly, it provides the prediction interval used to define if an observation is an anomaly, and it reports the associated z-score for each point. Get your token at https://nixtla.io/free-trial?utm_source=nixtla.io&amp;utm_campaign=/docs/api-reference.
+        /// This endpoint performs online anomaly detection based on the provided data. It uses cross-validation for more robust detection of anomalies and it supports detection for univariate and multivariate scenarios. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains a flag indicating if the date has an anomaly, it provides the prediction interval used to define if an observation is an anomaly, and it reports the associated z-score for each point. Get your token for private beta at https://www.nixtla.io/book-a-free-trial?utm_source=nixtla.io&amp;utm_campaign=/docs/api-reference.
         /// </summary>
         /// <param name="request"></param>
         /// <param name="requestOptions">Per-request overrides such as headers, query parameters, timeout, retries, and response buffering.</param>
@@ -32,7 +32,7 @@ public partial interface INixtlaClient
             global::System.Threading.CancellationToken cancellationToken = default);
         /// <summary>
         /// Foundational Time Series Model Online Multi Series Anomaly Detector<br/>
-        /// This endpoint performs online anomaly detection based on the provided data. It uses cross-validation for more robust detection of anomalies and it supports detection for univariate and multivariate scenarios. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains a flag indicating if the date has an anomaly, it provides the prediction interval used to define if an observation is an anomaly, and it reports the associated z-score for each point. Get your token at https://nixtla.io/free-trial?utm_source=nixtla.io&amp;utm_campaign=/docs/api-reference.
+        /// This endpoint performs online anomaly detection based on the provided data. It uses cross-validation for more robust detection of anomalies and it supports detection for univariate and multivariate scenarios. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains a flag indicating if the date has an anomaly, it provides the prediction interval used to define if an observation is an anomaly, and it reports the associated z-score for each point. Get your token for private beta at https://www.nixtla.io/book-a-free-trial?utm_source=nixtla.io&amp;utm_campaign=/docs/api-reference.
         /// </summary>
         /// <param name="series"></param>
         /// <param name="freq">
diff --git a/src/libs/Nixtla/Generated/Nixtla.NixtlaClient.V2AnomalyDetection.g.cs b/src/libs/Nixtla/Generated/Nixtla.NixtlaClient.V2AnomalyDetection.g.cs
index 967d7f4..d4397d0 100644
--- a/src/libs/Nixtla/Generated/Nixtla.NixtlaClient.V2AnomalyDetection.g.cs
+++ b/src/libs/Nixtla/Generated/Nixtla.NixtlaClient.V2AnomalyDetection.g.cs
@@ -43,7 +43,7 @@ partial void ProcessV2AnomalyDetectionResponseContent(
 
         /// <summary>
         /// Foundational Time Series Model Multi Series Anomaly Detector<br/>
-        /// Based on the provided data, this endpoint detects the anomalies in the historical perdiod of multiple time series at once. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains a flag indicating if the date has an anomaly and also provides the prediction interval used to define if an observation is an anomaly.Get your token at https://nixtla.io/free-trial?utm_source=nixtla.io&amp;utm_campaign=/docs/api-reference.
+        /// Based on the provided data, this endpoint detects the anomalies in the historical perdiod of multiple time series at once. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains a flag indicating if the date has an anomaly and also provides the prediction interval used to define if an observation is an anomaly.Get your token at https://www.nixtla.io/book-a-free-trial?utm_source=nixtla.io&amp;utm_campaign=/docs/api-reference.
         /// </summary>
         /// <param name="request"></param>
         /// <param name="requestOptions">Per-request overrides such as headers, query parameters, timeout, retries, and response buffering.</param>
@@ -66,7 +66,7 @@ partial void ProcessV2AnomalyDetectionResponseContent(
         }
         /// <summary>
         /// Foundational Time Series Model Multi Series Anomaly Detector<br/>
-        /// Based on the provided data, this endpoint detects the anomalies in the historical perdiod of multiple time series at once. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains a flag indicating if the date has an anomaly and also provides the prediction interval used to define if an observation is an anomaly.Get your token at https://nixtla.io/free-trial?utm_source=nixtla.io&amp;utm_campaign=/docs/api-reference.
+        /// Based on the provided data, this endpoint detects the anomalies in the historical perdiod of multiple time series at once. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains a flag indicating if the date has an anomaly and also provides the prediction interval used to define if an observation is an anomaly.Get your token at https://www.nixtla.io/book-a-free-trial?utm_source=nixtla.io&amp;utm_campaign=/docs/api-reference.
         /// </summary>
         /// <param name="request"></param>
         /// <param name="requestOptions">Per-request overrides such as headers, query parameters, timeout, retries, and response buffering.</param>
@@ -472,7 +472,7 @@ partial void ProcessV2AnomalyDetectionResponseContent(
         }
         /// <summary>
         /// Foundational Time Series Model Multi Series Anomaly Detector<br/>
-        /// Based on the provided data, this endpoint detects the anomalies in the historical perdiod of multiple time series at once. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains a flag indicating if the date has an anomaly and also provides the prediction interval used to define if an observation is an anomaly.Get your token at https://nixtla.io/free-trial?utm_source=nixtla.io&amp;utm_campaign=/docs/api-reference.
+        /// Based on the provided data, this endpoint detects the anomalies in the historical perdiod of multiple time series at once. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains a flag indicating if the date has an anomaly and also provides the prediction interval used to define if an observation is an anomaly.Get your token at https://www.nixtla.io/book-a-free-trial?utm_source=nixtla.io&amp;utm_campaign=/docs/api-reference.
         /// </summary>
         /// <param name="series"></param>
         /// <param name="freq">
diff --git a/src/libs/Nixtla/Generated/Nixtla.NixtlaClient.V2Finetune.g.cs b/src/libs/Nixtla/Generated/Nixtla.NixtlaClient.V2Finetune.g.cs
index 1a6b768..0f5b0e5 100644
--- a/src/libs/Nixtla/Generated/Nixtla.NixtlaClient.V2Finetune.g.cs
+++ b/src/libs/Nixtla/Generated/Nixtla.NixtlaClient.V2Finetune.g.cs
@@ -43,7 +43,7 @@ partial void ProcessV2FinetuneResponseContent(
 
         /// <summary>
         /// Foundational Time Series Model Multi Series Finetuning<br/>
-        /// Fine-tune the large time model to your data and save it for later use. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains the ID of the finetuned model, which you can provide in other endpoints to use that model to make the forecasts. Get your token at https://nixtla.io/free-trial?utm_source=nixtla.io&amp;utm_campaign=/docs/api-reference.
+        /// Fine-tune the large time model to your data and save it for later use. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains the ID of the finetuned model, which you can provide in other endpoints to use that model to make the forecasts. Get your token for private beta at https://www.nixtla.io/book-a-free-trial?utm_source=nixtla.io&amp;utm_campaign=/docs/api-reference.
         /// </summary>
         /// <param name="request"></param>
         /// <param name="requestOptions">Per-request overrides such as headers, query parameters, timeout, retries, and response buffering.</param>
@@ -66,7 +66,7 @@ partial void ProcessV2FinetuneResponseContent(
         }
         /// <summary>
         /// Foundational Time Series Model Multi Series Finetuning<br/>
-        /// Fine-tune the large time model to your data and save it for later use. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains the ID of the finetuned model, which you can provide in other endpoints to use that model to make the forecasts. Get your token at https://nixtla.io/free-trial?utm_source=nixtla.io&amp;utm_campaign=/docs/api-reference.
+        /// Fine-tune the large time model to your data and save it for later use. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains the ID of the finetuned model, which you can provide in other endpoints to use that model to make the forecasts. Get your token for private beta at https://www.nixtla.io/book-a-free-trial?utm_source=nixtla.io&amp;utm_campaign=/docs/api-reference.
         /// </summary>
         /// <param name="request"></param>
         /// <param name="requestOptions">Per-request overrides such as headers, query parameters, timeout, retries, and response buffering.</param>
@@ -472,7 +472,7 @@ partial void ProcessV2FinetuneResponseContent(
         }
         /// <summary>
         /// Foundational Time Series Model Multi Series Finetuning<br/>
-        /// Fine-tune the large time model to your data and save it for later use. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains the ID of the finetuned model, which you can provide in other endpoints to use that model to make the forecasts. Get your token at https://nixtla.io/free-trial?utm_source=nixtla.io&amp;utm_campaign=/docs/api-reference.
+        /// Fine-tune the large time model to your data and save it for later use. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains the ID of the finetuned model, which you can provide in other endpoints to use that model to make the forecasts. Get your token for private beta at https://www.nixtla.io/book-a-free-trial?utm_source=nixtla.io&amp;utm_campaign=/docs/api-reference.
         /// </summary>
         /// <param name="series"></param>
         /// <param name="freq">
diff --git a/src/libs/Nixtla/Generated/Nixtla.NixtlaClient.V2OnlineAnomalyDetection.g.cs b/src/libs/Nixtla/Generated/Nixtla.NixtlaClient.V2OnlineAnomalyDetection.g.cs
index 3edbca3..28ee2c8 100644
--- a/src/libs/Nixtla/Generated/Nixtla.NixtlaClient.V2OnlineAnomalyDetection.g.cs
+++ b/src/libs/Nixtla/Generated/Nixtla.NixtlaClient.V2OnlineAnomalyDetection.g.cs
@@ -43,7 +43,7 @@ partial void ProcessV2OnlineAnomalyDetectionResponseContent(
 
         /// <summary>
         /// Foundational Time Series Model Online Multi Series Anomaly Detector<br/>
-        /// This endpoint performs online anomaly detection based on the provided data. It uses cross-validation for more robust detection of anomalies and it supports detection for univariate and multivariate scenarios. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains a flag indicating if the date has an anomaly, it provides the prediction interval used to define if an observation is an anomaly, and it reports the associated z-score for each point. Get your token at https://nixtla.io/free-trial?utm_source=nixtla.io&amp;utm_campaign=/docs/api-reference.
+        /// This endpoint performs online anomaly detection based on the provided data. It uses cross-validation for more robust detection of anomalies and it supports detection for univariate and multivariate scenarios. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains a flag indicating if the date has an anomaly, it provides the prediction interval used to define if an observation is an anomaly, and it reports the associated z-score for each point. Get your token for private beta at https://www.nixtla.io/book-a-free-trial?utm_source=nixtla.io&amp;utm_campaign=/docs/api-reference.
         /// </summary>
         /// <param name="request"></param>
         /// <param name="requestOptions">Per-request overrides such as headers, query parameters, timeout, retries, and response buffering.</param>
@@ -66,7 +66,7 @@ partial void ProcessV2OnlineAnomalyDetectionResponseContent(
         }
         /// <summary>
         /// Foundational Time Series Model Online Multi Series Anomaly Detector<br/>
-        /// This endpoint performs online anomaly detection based on the provided data. It uses cross-validation for more robust detection of anomalies and it supports detection for univariate and multivariate scenarios. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains a flag indicating if the date has an anomaly, it provides the prediction interval used to define if an observation is an anomaly, and it reports the associated z-score for each point. Get your token at https://nixtla.io/free-trial?utm_source=nixtla.io&amp;utm_campaign=/docs/api-reference.
+        /// This endpoint performs online anomaly detection based on the provided data. It uses cross-validation for more robust detection of anomalies and it supports detection for univariate and multivariate scenarios. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains a flag indicating if the date has an anomaly, it provides the prediction interval used to define if an observation is an anomaly, and it reports the associated z-score for each point. Get your token for private beta at https://www.nixtla.io/book-a-free-trial?utm_source=nixtla.io&amp;utm_campaign=/docs/api-reference.
         /// </summary>
         /// <param name="request"></param>
         /// <param name="requestOptions">Per-request overrides such as headers, query parameters, timeout, retries, and response buffering.</param>
@@ -472,7 +472,7 @@ partial void ProcessV2OnlineAnomalyDetectionResponseContent(
         }
         /// <summary>
         /// Foundational Time Series Model Online Multi Series Anomaly Detector<br/>
-        /// This endpoint performs online anomaly detection based on the provided data. It uses cross-validation for more robust detection of anomalies and it supports detection for univariate and multivariate scenarios. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains a flag indicating if the date has an anomaly, it provides the prediction interval used to define if an observation is an anomaly, and it reports the associated z-score for each point. Get your token at https://nixtla.io/free-trial?utm_source=nixtla.io&amp;utm_campaign=/docs/api-reference.
+        /// This endpoint performs online anomaly detection based on the provided data. It uses cross-validation for more robust detection of anomalies and it supports detection for univariate and multivariate scenarios. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains a flag indicating if the date has an anomaly, it provides the prediction interval used to define if an observation is an anomaly, and it reports the associated z-score for each point. Get your token for private beta at https://www.nixtla.io/book-a-free-trial?utm_source=nixtla.io&amp;utm_campaign=/docs/api-reference.
         /// </summary>
         /// <param name="series"></param>
         /// <param name="freq">
diff --git a/src/libs/Nixtla/openapi.json b/src/libs/Nixtla/openapi.json
index 25f572b..56f29a9 100644
--- a/src/libs/Nixtla/openapi.json
+++ b/src/libs/Nixtla/openapi.json
@@ -76,7 +76,7 @@
     "/v2/anomaly_detection": {
       "post": {
         "summary": "Foundational Time Series Model Multi Series Anomaly Detector",
-        "description": "Based on the provided data, this endpoint detects the anomalies in the historical perdiod of multiple time series at once. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains a flag indicating if the date has an anomaly and also provides the prediction interval used to define if an observation is an anomaly.Get your token at https://nixtla.io/free-trial?utm_source=nixtla.io&utm_campaign=/docs/api-reference.",
+        "description": "Based on the provided data, this endpoint detects the anomalies in the historical perdiod of multiple time series at once. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains a flag indicating if the date has an anomaly and also provides the prediction interval used to define if an observation is an anomaly.Get your token at https://www.nixtla.io/book-a-free-trial?utm_source=nixtla.io&utm_campaign=/docs/api-reference.",
         "operationId": "v2_anomaly_detection_v2_anomaly_detection_post",
         "requestBody": {
           "content": {
@@ -169,7 +169,7 @@
     "/v2/online_anomaly_detection": {
       "post": {
         "summary": "Foundational Time Series Model Online Multi Series Anomaly Detector",
-        "description": "This endpoint performs online anomaly detection based on the provided data. It uses cross-validation for more robust detection of anomalies and it supports detection for univariate and multivariate scenarios. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains a flag indicating if the date has an anomaly, it provides the prediction interval used to define if an observation is an anomaly, and it reports the associated z-score for each point. Get your token at https://nixtla.io/free-trial?utm_source=nixtla.io&utm_campaign=/docs/api-reference.",
+        "description": "This endpoint performs online anomaly detection based on the provided data. It uses cross-validation for more robust detection of anomalies and it supports detection for univariate and multivariate scenarios. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains a flag indicating if the date has an anomaly, it provides the prediction interval used to define if an observation is an anomaly, and it reports the associated z-score for each point. Get your token for private beta at https://www.nixtla.io/book-a-free-trial?utm_source=nixtla.io&utm_campaign=/docs/api-reference.",
         "operationId": "v2_online_anomaly_detection_v2_online_anomaly_detection_post",
         "requestBody": {
           "content": {
@@ -672,7 +672,7 @@
     "/v2/finetune": {
       "post": {
         "summary": "Foundational Time Series Model Multi Series Finetuning",
-        "description": "Fine-tune the large time model to your data and save it for later use. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains the ID of the finetuned model, which you can provide in other endpoints to use that model to make the forecasts. Get your token at https://nixtla.io/free-trial?utm_source=nixtla.io&utm_campaign=/docs/api-reference.",
+        "description": "Fine-tune the large time model to your data and save it for later use. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains the ID of the finetuned model, which you can provide in other endpoints to use that model to make the forecasts. Get your token for private beta at https://www.nixtla.io/book-a-free-trial?utm_source=nixtla.io&utm_campaign=/docs/api-reference.",
         "operationId": "v2_finetune_v2_finetune_post",
         "requestBody": {
           "content": {