[AINode] Add Holt-Winters algorithm and fix related bugs

integration-test/src/test/java/org/apache/iotdb/ainode/it/AINodeBasicIT.java

@@ -176,6 +176,26 @@ public void ModelOperationTest() {
     }
   }
 
+  @Test
+  public void callInferenceTest2() {
+    String sql =
+        "CALL INFERENCE(_holtwinters, \"select s0 from root.AI.data\", predict_length=6, generateTime=true)";
+    try (Connection connection = EnvFactory.getEnv().getConnection();
+        Statement statement = connection.createStatement()) {
+      try (ResultSet resultSet = statement.executeQuery(sql)) {
+        ResultSetMetaData resultSetMetaData = resultSet.getMetaData();
+        checkHeader(resultSetMetaData, "Time,output0");
+        int count = 0;
+        while (resultSet.next()) {
+          count++;
+        }
+        assertEquals(6, count);
+      }
+    } catch (SQLException e) {
+      fail(e.getMessage());
+    }
+  }
+
   @Test
   public void callInferenceTest() {
     String sql =

integration-test/src/test/java/org/apache/iotdb/relational/it/schema/IoTDBDatabaseIT.java

@@ -537,11 +537,12 @@ public void testInformationSchema() throws SQLException {
           "model_id,",
           new HashSet<>(
               Arrays.asList(
-                  "_timerxl,",
                   "_STLForecaster,",
                   "_NaiveForecaster,",
-                  "_ARIMA,",
-                  "_ExponentialSmoothing,")));
+                  "_HoltWinters,",
+                  "_TimerXL,",
+                  "_ExponentialSmoothing,",
+                  "_ARIMA,")));
 
       TestUtils.assertResultSetEqual(
           statement.executeQuery(
@@ -658,9 +659,10 @@ public void testInformationSchema() throws SQLException {
           "model_id,",
           new HashSet<>(
               Arrays.asList(
-                  "_timerxl,",
+                  "_TimerXL,",
                   "_STLForecaster,",
                   "_NaiveForecaster,",
+                  "_HoltWinters,",
                   "_ARIMA,",
                   "_ExponentialSmoothing,")));
 

iotdb-core/ainode/ainode/core/constant.py

@@ -139,6 +139,7 @@ class ModelInputName(Enum):
 class BuiltInModelType(Enum):
     # forecast models
     ARIMA = "_arima"
+    HOLTWINTERS = "_holtwinters"
     EXPONENTIAL_SMOOTHING = "_exponentialsmoothing"
     NAIVE_FORECASTER = "_naiveforecaster"
     STL_FORECASTER = "_stlforecaster"

iotdb-core/ainode/ainode/core/model/built_in_model_factory.py

@@ -45,7 +45,7 @@ def get_model_attributes(model_id: str):
         attribute_map = arima_attribute_map
     elif model_id == BuiltInModelType.NAIVE_FORECASTER.value:
         attribute_map = naive_forecaster_attribute_map
-    elif model_id == BuiltInModelType.EXPONENTIAL_SMOOTHING.value:
+    elif model_id == BuiltInModelType.EXPONENTIAL_SMOOTHING.value or model_id == BuiltInModelType.HOLTWINTERS.value:
         attribute_map = exponential_smoothing_attribute_map
     elif model_id == BuiltInModelType.STL_FORECASTER.value:
         attribute_map = stl_forecaster_attribute_map
@@ -85,7 +85,7 @@ def fetch_built_in_model(model_id, inference_attributes):
     # build the built-in model
     if model_id == BuiltInModelType.ARIMA.value:
         model = ArimaModel(attributes)
-    elif model_id == BuiltInModelType.EXPONENTIAL_SMOOTHING.value:
+    elif model_id == BuiltInModelType.EXPONENTIAL_SMOOTHING.value or model_id == BuiltInModelType.HOLTWINTERS.value:
         model = ExponentialSmoothingModel(attributes)
     elif model_id == BuiltInModelType.NAIVE_FORECASTER.value:
         model = NaiveForecasterModel(attributes)

iotdb-core/confignode/src/main/java/org/apache/iotdb/confignode/persistence/ModelInfo.java

@@ -74,11 +74,14 @@ public class ModelInfo implements SnapshotProcessor {
 
   private static final Set<String> builtInAnomalyDetectionModel = new HashSet<>();
 
+  private static final int timerXLInputLength = 2880;
+
   static {
-    builtInForecastModel.add("_timerxl");
+    builtInForecastModel.add("_TimerXL");
     builtInForecastModel.add("_ARIMA");
     builtInForecastModel.add("_NaiveForecaster");
     builtInForecastModel.add("_STLForecaster");
+    builtInForecastModel.add("_HoltWinters");
     builtInForecastModel.add("_ExponentialSmoothing");
     builtInAnomalyDetectionModel.add("_GaussianHMM");
     builtInAnomalyDetectionModel.add("_GMMHMM");
@@ -269,6 +272,9 @@ public GetModelInfoResp getModelInfo(GetModelInfoPlan plan) {
       // check if it's a built-in model
       if ((modelType = checkModelType(modelName)) != ModelType.USER_DEFINED) {
         modelInformation = new ModelInformation(modelType, modelName);
+        if (modelName.equalsIgnoreCase("_timerxl")) {
+          modelInformation.setInputLength(timerXLInputLength);
+        }
       } else {
         modelInformation = modelTable.getModelInformationById(modelName);
       }

iotdb-core/datanode/src/main/java/org/apache/iotdb/db/queryengine/plan/relational/function/tvf/ForecastTableFunction.java

@@ -333,7 +333,7 @@ public TableFunctionAnalysis analyze(Map<String, Argument> arguments) {
         }
       }
     } else {
-      String[] predictedColumnsArray = predicatedColumns.split(",");
+      String[] predictedColumnsArray = predicatedColumns.split(";");
       Map<String, Integer> inputColumnIndexMap = new HashMap<>();
       for (int i = 0, size = allInputColumnsName.size(); i < size; i++) {
         Optional<String> fieldName = allInputColumnsName.get(i);