## 5 Experiments
### Comparative Experiments and Results  
To further validate the effectiveness and superiority of the TransTLA model for home appliance sales forecasting, comparative experiments are conducted with several time series forecasting models to comprehensively assess the performance of the TransTLA model. In addition to the proposed TransTLA model, the comparison includes the following models:  
ARIMA: Autoregressive Integrated Moving Average, a classic and commonly used statistical method that predicts by capturing linear dependencies in time series data. XGBoost: Extreme Gradient Boosting, an advanced and scalable ML method that enhances model performance by combining the predictions of multiple weak learners and using gradient descent to minimize errors. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Compared Model** & **Step Size 1** & **Step Size 2** & **Step Size 3** & **Step Size 4** & **Average** \\ \hline TCN & 6.66 & 11.17 & 4.25 & 6.13 & 7.05 \\ LSTM & 8.55 & 35.04 & 30.50 & 36.31 & 27.60 \\ TCN-LSTM-Attention & 5.25 & 2.13 & 3.68 & 10.64 & 5.43 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The MAE performance improvement of the TransTLA model over the ablation models (percentage). SVR: Support Vector Regression, a ML method that extends Support Vector Machines (SVMs) to regression tasks, aiming to find a function within a margin of error while handling nonlinear relationships using kernel functions. RNN: A type of neural network designed for sequence data, characterized by maintaining states in the hidden layers to influence subsequent outputs with previous information. In the experiment, a two-layer RNN stack processes the input, followed by two fully connected layers to produce the forecast. CNN: A DL model initially used in image recognition but also effective in extracting local features of time series through one-dimensional convolution. The CNN structure employs one-dimensional convolution, average pooling, and ReLU activation function, stacked twice, followed by two fully connected layers for the output. CNN-LSTM [18]: A combined model that first extracts local features with CNN layers and then captures temporal dependencies with LSTM layers to enhance the forecasting performance. CNN-LSTM-Attention: A model that further combines the advantages of CNN, LSTM, and attention mechanisms to capture complex patterns and dependencies in time series data through layered abstraction and precise information filtering. TransCNN-LSTM-Attention: A model that incorporates TL strategies into CNN-LSTM-Attention to explore the impact of TL on sales forecasting performance. Experiments to predict sales volume for 1, 2, 3, and 4 days in advance are designed for each model. The results of various evaluation metrics in the comparative experiment are shown in Table 8, and Figure 13 displays the MAE comparison of different models across various forecast horizons. The experimental results from Table 8 and Figure 12 demonstrate that, as the forecast horizon lengthens, the predictive performance of all models declines due to the increasing uncertainty associated with making longer-term predictions. Overall, ML- and DL-based models outperform the traditional statistical ARIMA model. Across all evaluation metrics  
Figure 13: MAE comparison of comparative experiments. and forecast steps, RNN, CNN, and their variants consistently perform significantly better than ARIMA. Particularly at the forecast steps of 3 and 4 days, for ARIMA, the values of R\({}^{2}\) drop to \(-0.292\) and \(-0.723\), respectively, indicating that its predictions are less effective than a simple mean forecast and exhibit a substantial negative correlation. This suggests that traditional statistical models struggle to effectively capture the nonlinear trends and complex patterns in the time series data of home appliance sales. For ML-based methods, XGBoost and SVR show nuanced differences in their predictive performances over 1-4 day forecast horizons.