diff --git a/project/benchmarks/run_visionts.py b/project/benchmarks/run_visionts.py
index 5fb229e..82bf046 100644
--- a/project/benchmarks/run_visionts.py
+++ b/project/benchmarks/run_visionts.py
@@ -59,7 +59,7 @@ def norm_freq_str(freq_str: str) -> str:
 
 def get_seasonality_list(freq: str) -> int:
     offset = pd.tseries.frequencies.to_offset(freq)
-    base_seasonality_list = POSSIBLE_SEASONALITIES.get(norm_freq_str(offset.name), 1)
+    base_seasonality_list = POSSIBLE_SEASONALITIES.get(norm_freq_str(offset.name), [])
     seasonality_list = []
     for base_seasonality in base_seasonality_list:
         seasonality, remainder = divmod(base_seasonality, offset.n)
@@ -156,12 +156,11 @@ def evaluate(
                 best_valid_mae = float("inf")
                 best_valid_p = 1
                 for periodicity in seasonality_list:
-                    # Round context length to the integer multiples of the period
-                    context_len = convert_context_len(
+                    cur_context_len = convert_context_len(
                         context_len, no_periodicity_context_len, periodicity
                     )
                     model.update_config(
-                        context_len,
+                        cur_context_len,
                         prediction_length,
                         periodicity,
                         norm_const,
@@ -177,7 +176,7 @@ def evaluate(
                             [x["target"][-prediction_length:] for x in batch]
                         )
                         cur_forecast_samples = forcast_batch(
-                            input_batch, device, context_len, align_const, model
+                            input_batch, device, cur_context_len, align_const, model
                         )
                         assert cur_forecast_samples.shape == label_batch.shape
                         cur_mae_list.append(np.abs(label_batch - cur_forecast_samples))
@@ -198,15 +197,14 @@ def evaluate(
             else:
                 periodicity = int(periodicity)
 
-            print(f"Use periodicity = {periodicity}, context len = {context_len}")
-
             # Generate forecast samples
             forecast_samples = []
-            context_len = convert_context_len(
+            cur_context_len = convert_context_len(
                 context_len, no_periodicity_context_len, periodicity
             )
+            print(f"Use periodicity = {periodicity}, context len = {cur_context_len}")
             model.update_config(
-                context_len, prediction_length, periodicity, norm_const, align_const
+                cur_context_len, prediction_length, periodicity, norm_const, align_const
             )
             for batch in tqdm(
                 list(batcher(test_data.input, batch_size=batch_size)),
@@ -214,7 +212,7 @@ def evaluate(
             ):
                 batch = [x["target"] for x in batch]
                 cur_forecast_samples = forcast_batch(
-                    batch, device, context_len, align_const, model
+                    batch, device, cur_context_len, align_const, model
                 )
                 forecast_samples.append(cur_forecast_samples)
             forecast_samples = np.concatenate(forecast_samples, axis=0)