diff --git a/darts/ad/detectors/threshold_detector.py b/darts/ad/detectors/threshold_detector.py
index 7a0eac4324..1d89d90a99 100644
--- a/darts/ad/detectors/threshold_detector.py
+++ b/darts/ad/detectors/threshold_detector.py
@@ -80,8 +80,8 @@ def _detect_core(self, series: TimeSeries, name: str = "series") -> TimeSeries:
 
         def _detect_fn(x, lo, hi):
             # x of shape (time,) for 1 component
-            return (x < (np.NINF if lo is None else lo)) | (
-                x > (np.Inf if hi is None else hi)
+            return (x < (-np.inf if lo is None else lo)) | (
+                x > (np.inf if hi is None else hi)
             )
 
         detected = np.zeros_like(np_series, dtype=int)
diff --git a/darts/models/forecasting/fft.py b/darts/models/forecasting/fft.py
index 35d2f67583..2143c917c5 100644
--- a/darts/models/forecasting/fft.py
+++ b/darts/models/forecasting/fft.py
@@ -356,7 +356,7 @@ def fit(self, series: TimeSeries):
         ]
 
         # set all other values in the frequency domain to 0
-        self.fft_values_filtered = np.zeros(len(self.fft_values), dtype=np.complex_)
+        self.fft_values_filtered = np.zeros(len(self.fft_values), dtype=np.complex128)
         self.fft_values_filtered[self.filtered_indices] = self.fft_values[
             self.filtered_indices
         ]
diff --git a/darts/models/forecasting/forecasting_model.py b/darts/models/forecasting/forecasting_model.py
index dfcbe9de13..c191fd1e3d 100644
--- a/darts/models/forecasting/forecasting_model.py
+++ b/darts/models/forecasting/forecasting_model.py
@@ -1120,7 +1120,7 @@ def retrain_func(
                             length=1,
                             freq=series_.freq,
                         ),
-                        values=np.array([np.NaN]),
+                        values=np.array([np.nan]),
                     )
 
                 forecast = model._predict_wrapper(
diff --git a/darts/utils/losses.py b/darts/utils/losses.py
index 2c51e71145..948660e791 100644
--- a/darts/utils/losses.py
+++ b/darts/utils/losses.py
@@ -17,7 +17,7 @@ def _divide_no_nan(a, b):
     result = a / b
     result[result != result] = 0.0
     result[result == np.inf] = 0.0
-    result[result == np.NINF] = 0.0
+    result[result == -np.inf] = 0.0
     return result