Algorithm 1 Inference Time Calculation
1: procedure calculateInferenceTime($model$, $X_{\mathrm{test}}$)
2:      Variables:
3:         $input\_data$, $start\_time$, $prediction\_inference\_time$, $mean\_time$: float;
4:         times: list;
5:         i: int;
6:
7:      for $i\le \mathrm{length}\left(X_{\mathrm{test}}\right)$ do
8:            $input\_data=X_{\mathrm{test}}\left[i\right]$	▹ Get the input data for the current sample
9:            $input\_data=\mathrm{np}.\mathrm{newaxis}(input\_data,\mathrm{axis}=0)$	▹ Reshape the input to match the expected shape
10:            $start\_time=\mathrm{time.perf\_counter()}$	▹ Record the start time
11:            $prediction=\mathrm{model}.\mathrm{predict}(input\_data)$	▹ Perform the forward pass to get the prediction
12:            $inference\_time=\mathrm{time.perf\_counter()}-start\_time$	▹ Calculate the inference time
13:            $\mathrm{times}.\mathrm{append}(inference\_time\times 1000)$	▹ Append the inference time to the list
14:      end for
15:      $mean\_time=\mathrm{np}.\mathrm{mean}\left(\mathrm{times}\right)$	▹ Calculate the mean inference time
16:      $std\_time=\mathrm{np}.\mathrm{std}\left(\mathrm{times}\right)$	▹ Calculate the standard deviation of inference times
17:      return $mean\_time$, $std\_time$
18: end procedure