## 5 Experiments
### Ablation Experiments and Results  
To comprehensively evaluate the contribution of each component in the TransTLA model to the overall performance of home appliance sales forecasting, this paper conducts a series of ablation experiments. The essence of ablation experiments is to remove or modify key components of the model gradually, compare the performance of the complete model  
Figure 11: Home appliance sales forecast results under different prediction steps. with the ablated models in forecasting tasks, and thus deeply understand and quantify the specific impact of these components on the overall model performance. The experiments focus on four key parts, i.e., TCN layers, LSTM layers, attention mechanism layers, and TL mechanisms, and various combinations of these parts, respectively. Notably, when TL is not used, the model is trained and predicted using only the target domain dataset. Models with the prefix \"Trans\" indicate the use of TL strategies, which means training with both source and target domain data and evaluating the testing set of the target domain. The ablation experiments in this study cover a time span of 1 to 4 days in the future to fully analyze the impact of each component on the model's forecasting ability. Through these ablation experiments, we can not only identify the model components that are crucial for the forecasting performance but also understand whether the role of these components differs significantly under different prediction time spans. To address the inherent stochasticity in DL models due to random weight initialization and optimization processes, we conducted 10 separate evaluations for each model. Table 6 lists the average evaluation metrics of the models under different ablation configurations, with the standard deviation indicated in brackets. Figure 12 visually displays how the MAE metric of different models changes with the time span. Additionally, to statistically confirm the significant differences in the performance of the models, we performed independent \(t\)-tests, considering a significance level of \(p<0.05\). Figure 12: MAE comparison of the ablation experiments. decline is due to escalating uncertainty and the diminishing relevance of historical data to future sales over time. In single model structures, TCN and LSTM perform relatively well for a forecast step of one but deteriorate rapidly as the steps increase. Particularly, LSTM's R2 becomes negative at step 4, indicating its predictions are less effective than a simple mean forecast and have very low relevance to the actual sales data. Thus, LSTM cannot effectively capture future trends. In contrast, TCN shows some predictive power at step 4. Furthermore, the TCN-LSTM model shows some advantages at steps 1 and 2 but has poor performance at steps 3 and 4, indicating limitations in handling long-term forecasting tasks. However, the introduction of the attention mechanism improves the performance of the TCN-Attention, LSTM-Attention, and TCN-LSTM-Attention model, which achieves better results across single and multiple forecast steps, demonstrating that the attention mechanism helps the model focus on key information in the time series and confirms the advantage of hybrid model structures in capturing complex time series features. The introduction of TL mechanisms enhances the model's predictive performance. Such improvement can be attributed that leveraging the source domain data can effectively enhance the model's understanding of the target domain. Comparisons clearly show that the proposed TransTLA model outperforms other ablation models at most forecast steps, with the percentage MAE improvement displayed in Table 7. At a one-day forecast step, the TransTLA model reduces the MAE by 6.66% compared to the TCN model and by 8.55% compared to the LSTM model, with 5.25% improvement over the non-TL TCN-LSTM-Attention model.