diff --git a/fedot/core/data/data.py b/fedot/core/data/data.py
index 3a33a1ad33..db2761d244 100644
--- a/fedot/core/data/data.py
+++ b/fedot/core/data/data.py
@@ -442,6 +442,9 @@ def slice(self, start: Optional[int] = None, stop: Optional[int] = None,
 
         index = np.arange(len(self))[slice(start, stop, step)]
 
+        if len(index) == 0:
+            raise ValueError(f"Uncorrect slicing [{start}:{stop}:{step}] for data with length {len(self)}")
+
         return self.slice_by_index(index, step, in_sample=in_sample)
 
     def slice_by_index(self, indexes: Iterable, step: int = 1, in_sample: bool = True):
diff --git a/fedot/core/pipelines/ts_wrappers.py b/fedot/core/pipelines/ts_wrappers.py
index a2a94b451b..acaf51f137 100644
--- a/fedot/core/pipelines/ts_wrappers.py
+++ b/fedot/core/pipelines/ts_wrappers.py
@@ -89,16 +89,19 @@ def in_sample_ts_forecast(pipeline, input_data: Union[InputData, MultiModalData]
     number_of_iterations = math.ceil(horizon / forecast_length)
 
     final_forecast = np.zeros((number_of_iterations, forecast_length))
+    data = InputData(idx=input_data.idx,
+                     features=input_data.features[:-forecast_length],
+                     target=input_data.target,
+                     data_type=input_data.data_type,
+                     task=input_data.task)
     for i in range(number_of_iterations):
-        data = input_data.slice(-(i + 1) * forecast_length,
-                                -i * forecast_length if i != 0 else None,
-                                in_sample=False)
-        if data.features.shape[0] == 0:
-            raise ValueError(('Cannot make predict without features.'
-                              'Please, check that horizon is lower than data.features'))
         iter_predict = pipeline.predict(input_data=data)
-        iter_predict = np.ravel(iter_predict.predict)
-        final_forecast[-(i + 1), :] = iter_predict
+        final_forecast[-(i + 1), :] = np.ravel(iter_predict.predict)
+        data = InputData(idx=data.idx[:-forecast_length],
+                         features=data.features[:-forecast_length],
+                         target=data.target[:-forecast_length],
+                         data_type=data.data_type,
+                         task=data.task)
 
     final_forecast = np.ravel(final_forecast)[:horizon]
     return final_forecast
diff --git a/test/unit/tasks/test_forecasting.py b/test/unit/tasks/test_forecasting.py
index 0c5c51e183..314c74fa21 100644
--- a/test/unit/tasks/test_forecasting.py
+++ b/test/unit/tasks/test_forecasting.py
@@ -271,19 +271,29 @@ def test_multistep_out_of_sample_forecasting():
 
 def test_multistep_in_sample_forecasting():
     horizon = 12
-    train_data, test_data = get_ts_data(n_steps=200, forecast_length=5)
+    train_data, test_data = get_ts_data(n_steps=200, forecast_length=5, validation_blocks=3)
 
     pipeline = get_multiscale_pipeline()
 
     # Fit pipeline to make forecasts 5 elements above
     pipeline.fit(input_data=train_data)
 
+    # manual predict
+    a = test_data.slice(-15, -10, in_sample=False)
+    manual_predict = []
+    manual_predict.append(pipeline.predict(input_data=test_data.slice(-15, -10, in_sample=False)))
+    manual_predict.append(pipeline.predict(input_data=test_data.slice(-10, -5, in_sample=False)))
+    manual_predict.append(pipeline.predict(input_data=test_data.slice(-5, in_sample=False)))
+    manual_predict = [np.ravel(x.predict) for x in manual_predict]
+    manual_predict = np.concatenate(manual_predict, axis=0)[:horizon]
+
     # Make prediction for 12 elements
     predicted = in_sample_ts_forecast(pipeline=pipeline,
                                       input_data=test_data,
                                       horizon=horizon)
 
     assert len(predicted) == horizon
+    assert np.array_equal(manual_predict, predicted)
 
 
 def test_ts_forecasting_with_multiple_series_in_lagged():