# A Predictive Method for Temperature Based on Ensemble EMD with Linear Regression
## 6 Conclusions and Future Work  
An innovative hybrid approach, EEMD-LR, is introduced for temperature time-series forecasting, integrating LR and EEMD methodologies. Initially, the EEMD algorithm decomposes the temperature time series into distinct components, each representing a simpler, more regular, and more stable sub-sequence compared to the initial series. Subsequently, the LR method is applied to forecast each sub-component signal. These predicted values are then aggregated to derive the final predictions for the initial temperature series. Two datasets were employed to evaluate the efficacy of our proposed method against nine other techniques, with our proposed method demonstrating a superior predictive performance. While our approach yielded promising results in experimentation, it also exhibited certain limitations, notably in forecasting sequences with abrupt changes. The field of time-series analysis and forecasting methods has experienced rapid development, yet its effectiveness often falls short of meeting the rigorous demands of practical applications in various fields. Numerous challenges persist, indicating areas for further research and improvement. Despite the efforts outlined in this paper, several avenues remain open for future investigation:  
1. This paper employs the EEMD method to decompose intricate time series, yielding some success. Yet, its predictive efficacy alters when the proposed method is applied to time series with abrupt fluctuations. To address this issue, future endeavors should focus on refining the EEMD method or integrating it with complementary techniques, such as LSTM and transformer methods. This collaborative approach aims to enhance the prediction accuracy for time series characterized by significant variability;
2. This paper evaluates only nine machine learning techniques for sequence prediction. To facilitate more comprehensive comparative research, future endeavors should explore additional machine learning or deep learning methodologies, for example, models based on LSTM and transformer methodologies. Conducting experiments will enable a thorough understanding of each method's strengths and weaknesses, aiding in informed selection based on specific practical scenarios;
3. The experimental outcomes presented in this paper are promising; however, they fall short of our anticipated results. A preliminary analysis of potential factors contributing to this discrepancy is outlined herein, highlighting areas for future investigation and enhancement. Moving forward, our focus will center on refining and amalgamating multiple algorithms to enhance the sequence forecasting efficacy in real-world scenarios. Additionally, leveraging high-performance computing resources, increasing the number of training iterations, and expanding the test dataset size will be paramount to constructing a more robust and applicable learning model. The EEMD-LR framework faces challenges in highly volatile scenarios (e.g., abrupt regime shifts or extreme events), where EEMD may suffer from residual mode mixing, that compromises its decomposition stability and LR components might inadequately capture transient nonlinear dynamics. For future work, we propose the following: (1) integrating adaptive noise-assisted EEMD variants (e.g., CEEMDAN) to enhance the model's robustness in high-variability contexts; (2) replacing LR with attention-based deep learning modules (e.g., transformers) to model complex temporal dependencies; (3) developing an uncertainty quantification mechanism for probabilistic forecasts. To strengthen the model's practical relevance, we will anchor discussions to concrete real-world applications: (a) short-term renewable energy forecasting (e.g., solar/wind generation under erratic weather) and addressing intermittency management for grid stability; (b) high-frequency financial volatility prediction (e.g., cryptocurrency markets) to support algorithmic trading risk control.