## 4 Performance Evaluation
### Tcn-Ts-SW Forecasting  
In this experiment, the effect of adjusting the time window length \(\Delta\) is investigated. Also, in this section, it will highlight the justification of the reason behind the proposed two-stage sliding window technique. A different dataset is used in this experiment and follows the setup as specified in Table 3 however, the sampling rate on the data acquisition device is adjusted to 400 samples/second. Note that the sample acquisition is conducted within 5 s interval. The dataset is composed of raw sensor readings from the acquisition device, hence the temperature readings are still on voltages. For uniformity, the same set of dataset is used all throughout the experiment. The TCN-TS-SW can compute for the historical forecasts the model that returns a time series created from the last point of each of these individual forecasts. It trains the current model on the training set, emits a forecast of length equal to forecast horizon, and then moves the end of the training set forward by stride time steps. The term start, refers to the first point of time at which a prediction is computed for a future time-step. The numerical value pertains to the proportion of the series that should be present before the first prediction point. In building the forecasting parameters of the model: we need to define that the \(m=\) start * \(\Delta\). Input length refers to the past time steps that are fed to the forecasting module. Discussing further, a window length of \(\Delta=300\) and start = 0.8 then, the input length \(m=240\), that is also equal to the length of target series (refer to the Figure 1). With that determined, the number of elements forecasted, forecast horizon \(h\) is equal  
Figure 5: The effectiveness of two-stage sliding window strategy when applied to the temporal neural network architecture. (**a**) The proposed scheme’s vs TCN in terms of training loss over 40 epochs. (**b**) The effectiveness of the proposed system versus the later in terms of training accuracy. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & \(\Delta=\) **300** & \(\Delta=\) **400** \\ \hline Training/validation ratio & 70/30 & 70/30 \\ Random State & 0 & 0 \\ Epoch & 5 & 5 \\ Stride & 1 & 1 \\ \hline Residual Block & & \\ \hline Number of filters & 6 & 6 \\ Number of layers & 3 & 4 \\ Weight Normalization & True & True \\ Dilation base & 2 & 2 \\ Dropout & 0.5 & 0.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: TCN-TS-SW System Parameters. to \(h=(1-\text{start})\,^{*}\,\Delta\) referring to the number of elements that is forecasted in one loop of execution. For the scenario I structure, it is summarized in Table 3. The window size \(\Delta=300\) and note that \(\delta\) is equal to \(\Delta\). For the second stage of sliding window technique, the training and validation data is split to 70-to-30 percent ratio. Note that this ratio does not affect the activity of the sliding window technique. The random state value refers to the degree of randomness of the initialized weights. This network characteristic contributes as a factor on the difference on the simulation results. The training epoch is set to 5 and it trains the current model with the training set, emits a forecast of length equal to forecast horizon, and then moves the end of the training set by stride time steps. For the residual blocks: the kernel size is equal to 3, number of filters is equal to the number of residual blocks necessary to achieve full receptive field coverage, computed using the Equation (1). Finally, the dropout rate is applicable for every convolutional layer throughout the network. As can be seen in Figure 6 the alongside comparison of the actual data versus the predicted one. The forecasting module of the proposed TCN-TS-SW performs satisfactorily to reproduce the actual data. It is because of a low number of epoch and a relatively small number of training data.