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电价预测模型 — 独立项目 时间：2024年暑期 基于时间序列与深度学习模型（LSTM、Transformer）构建电价预测系统，探索短期与中长期预测的差异化效果； 实现数据预处理与特征工程（缺失值处理、归一化、滞后特征构造、节假日与天气特征融合），提升模型鲁棒性； 在实验中对比了ARIMA、XGBoost与改进的Transformer结构，并以MAPE、RMSE为指标进行量化评估； 使用PyTorch + sklearn 完成模型搭建与训练，并基于可视化分析解释预测结果，撰写完整技术报告； 项目代码与报告开源至GitHub，积累实践经验并初步形成科研表达能力 Summer 2024 Developed a time-series forecasting system for electricity prices using LSTM and Transformer-based models, comparing short-term and long-term forecasting performance. Designed and implemented data preprocessing & feature engineering, including missing value imputation, normalization, lag features, and integration of holiday/weather factors. Conducted benchmarking with ARIMA, XGBoost, and enhanced Transformer variants, evaluated via MAPE and RMSE metrics. Built and trained models using PyTorch and scikit-learn, with visualization for interpretability and a full technical report. Open-sourced project on GitHub, gaining hands-on experience in applied machine learning and technical reportin