标题： 基于模式相似性的机器学习方法用于中期负荷预测：比较研究 显示英文标题

标题： Pattern Similarity-based Machine Learning Methods for Mid-term Load Forecasting: A Comparative Study

摘要： 基于模式相似性的方法广泛用于分类和回归问题。 在季节性时间序列中观察到的重复、相似形状的周期鼓励应用这些方法进行预测。 在本文中，我们使用基于模式相似性的方法来预测月度电力需求，表现出年度季节性。 模型的一个重要部分是使用时间序列序列的模式来表示时间序列。 模式表示通过趋势过滤和方差均衡确保输入和输出数据的统一性。 因此，模式表示简化了预测问题，并使我们能够使用基于模式相似性的模型。 我们考虑了四种这样的模型：最近邻模型、模糊邻域模型、核回归模型和通用回归神经网络。 通过根据输入模式之间的相似性对输出模式进行加权聚合来构建回归函数。 所提出模型的优势包括：操作原理清晰、需要调整的参数数量少、优化过程快速、泛化能力好、在不重新训练的情况下处理最新数据、对缺失输入变量具有鲁棒性，并且生成向量作为输出。 在实验部分，所提出的模型用于预测35个欧洲国家的月度需求。 模型性能与经典模型如ARIMA和指数平滑以及最先进的模型如多层感知器、神经模糊系统和长短期记忆模型的性能进行了比较。 结果表明所提出模型具有高性能，在准确性、简单性和优化的易用性方面优于比较模型。 显示英文摘要

摘要： Pattern similarity-based methods are widely used in classification and regression problems. Repeated, similar-shaped cycles observed in seasonal time series encourage to apply these methods for forecasting. In this paper we use the pattern similarity-based methods for forecasting monthly electricity demand expressing annual seasonality. An integral part of the models is the time series representation using patterns of time series sequences. Pattern representation ensures the input and output data unification through trend filtering and variance equalization. Consequently, pattern representation simplifies the forecasting problem and allows us to use models based on pattern similarity. We consider four such models: nearest neighbor model, fuzzy neighborhood model, kernel regression model and general regression neural network. A regression function is constructed by aggregation output patterns with weights dependent on the similarity between input patterns. The advantages of the proposed models are: clear principle of operation, small number of parameters to adjust, fast optimization procedure, good generalization ability, working on the newest data without retraining, robustness to missing input variables, and generating a vector as an output. In the experimental part of the work the proposed models were used to forecasting the monthly demand for 35 European countries. The model performances were compared with the performances of the classical models such as ARIMA and exponential smoothing as well as state-of-the-art models such as multilayer perceptron, neuro-fuzzy system and long short-term memory model. The results show high performance of the proposed models which outperform the comparative models in accuracy, simplicity and ease of optimization.