Supporting estimation of "ts"

verticapy/machine_learning/vertica/tsa.py

@@ -339,6 +339,7 @@ def predict(
         npredictions: int = 10,
         output_standard_errors: bool = False,
         output_index: bool = False,
+        output_estimated_ts: bool = False,
     ) -> vDataFrame:
         """
         Predicts using the input relation.
@@ -394,7 +395,11 @@ def predict(
             Boolean,  whether to return estimates  of the standard
             error of each prediction.
         output_index: bool, optional
-            Boolean,  whether to return the index of each position.
+            Boolean, whether to return the index of each prediction.
+        output_estimated_ts: bool, optional
+            Boolean, whether to return the estimated abscissa of
+            each prediction. The real one is hard to obtain due to
+            interval computations.
 
         Returns
         -------
@@ -418,24 +423,31 @@ def predict(
             y=y,
             start=start,
             npredictions=npredictions,
-            output_standard_errors=output_standard_errors,
+            output_standard_errors=(
+                output_standard_errors or output_index or output_estimated_ts
+            ),
             output_index=output_index,
         )
         no_relation = True
         if not (isinstance(vdf, NoneType)):
             sql += f" FROM {vdf}"
             no_relation = False
-        if output_index:
+        if output_index or output_estimated_ts:
             j = self.n_
             if no_relation:
                 if not (isinstance(start, NoneType)):
                     j = j + start
             elif not (isinstance(start, NoneType)):
                 j = start
-            if output_standard_errors and not (ar_ma):
+            if (output_standard_errors or output_estimated_ts) and not (ar_ma):
+                if not (output_standard_errors):
+                    stde_out = ""
+                else:
+                    stde_out = ", std_err"
                 output_standard_errors = ", std_err"
             else:
                 output_standard_errors = ""
+                stde_out = ""
             if ar_ma:
                 order_by = ""
             else:
@@ -445,7 +457,21 @@ def predict(
                     ROW_NUMBER() OVER ({order_by}) + {j} - 1 AS idx,
                     prediction{output_standard_errors}
                 FROM ({sql}) VERTICAPY_SUBTABLE"""
-        return vDataFrame(sql)
+        if output_estimated_ts:
+            min_value = f"(SELECT MIN({self.ts}) FROM {self.input_relation})"
+            delta = f"""
+                (SELECT 
+                    AVG(delta) 
+                 FROM (SELECT 
+                        {self.ts} - LAG({self.ts}) OVER (ORDER BY {self.ts}) AS delta 
+                       FROM {self.input_relation}) VERTICAPY_SUBTABLE)"""
+            sql = f"""
+                SELECT
+                    {delta} + idx * {min_value} AS {self.ts},
+                    prediction{stde_out}
+                FROM ({sql}) VERTICAPY_SUBTABLE
+            """
+        return vDataFrame(clean_query(sql))
 
     # Model Evaluation Methods.