diff --git a/examples/timeseries/anomaly.py b/examples/timeseries/anomaly.py
new file mode 100644
index 0000000000..0476923012
--- /dev/null
+++ b/examples/timeseries/anomaly.py
@@ -0,0 +1,18 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+import torch
+
+import sdm
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+time = torch.arange(200, device=device, dtype=torch.float32)
+x = torch.stack([torch.sin(time / 12), torch.cos(time / 20)], dim=-1)
+x[140:149] += 3  # Add anomalies.
+
+model = sdm.models.KumoAnomaly(device=device)
+
+# Higher reconstruction error scores indicate more likely anomalies:
+score = model(x).numerical.squeeze(-1)
+print(score[131:158])
diff --git a/examples/timeseries/quickstart.py b/examples/timeseries/forecast.py
similarity index 100%
rename from examples/timeseries/quickstart.py
rename to examples/timeseries/forecast.py
diff --git a/sdm/models/kumo/timeseries/anomaly/model.py b/sdm/models/kumo/timeseries/anomaly/model.py
index e24c35a047..f15f06a94d 100644
--- a/sdm/models/kumo/timeseries/anomaly/model.py
+++ b/sdm/models/kumo/timeseries/anomaly/model.py
@@ -256,8 +256,9 @@ def forward(
             dim=0,
         )
 
-        out = out.view(x.size())[:, :R, :D]
-        out = (out - x[:, :R, :D]).abs().mean(dim=-1)
+        out = out.view(x.size())[:, :R, :D].clamp(-1e8, 1e8)
+        x = x[:, :R, :D].clamp(-1e8, 1e8)
+        out = (out - x).abs().mean(dim=-1).clamp(max=1e8)
         return out.view(*B, R)
 
     def reconstruct(