vmd - lstm

用vmd - lstm 预测 菜的销量VMD是一种信号处理技术，用于将复杂的时间序列信号分解成多个局部频率模式，这些模式可以反映信号中的不同频率成分和振幅变化。VMD的主要思想是通过优化问题，将信号分解成多个本征模态函数（EMD），这些函数具有不同的频率和振幅特征，并且在一定程度上是正交的，与传统的信号分解方法相比，VMD能够更好地适应非线性和非平稳信号，并且具有更好的局部特征提取能力。VMD在多个领域中得到了广泛的应用，包括信号处理、生物医学工程、地震学等 VMD-LSTM将VMD用于对原始时间序列进行分解，得到多个本征模态函数，每个本征模态函数代表了原始信号在不同频率和振幅上的成分，具有更好的局部特征表示，将这些本征模态函数作为输入序列，每个模态分别构建一个LSTM预测模型进行学习预测，从而提高模型的预测性能Predicting Vegetable Sales Using VMD-LSTM. Variational Mode Decomposition (VMD) is a signal processing technique that decomposes complex time-series signals into multiple local frequency patterns, which can reflect different frequency components and amplitude variations of the signals. The core principle of VMD is to decompose a signal into multiple intrinsic mode functions (IMFs) via optimization problems. These functions have distinct frequency and amplitude characteristics and are orthogonal to a certain extent. Compared with traditional signal decomposition methods, VMD can better adapt to nonlinear and non-stationary signals and exhibits superior local feature extraction capabilities. VMD has been widely applied in various fields, including signal processing, biomedical engineering, seismology, and others. The VMD-LSTM framework applies VMD to decompose the original time series into multiple intrinsic mode functions. Each IMF represents the components of the original signal at different frequencies and amplitudes, enabling better local feature representation. These IMFs are taken as input sequences, and an individual LSTM prediction model is constructed for each mode to perform learning and prediction, thereby improving the overall prediction performance of the model.

搜集汇总

背景与挑战

背景概述

该数据集用于预测菜品销量，采用VMD-LSTM方法，其中VMD技术分解时间序列信号以提取局部特征，LSTM模型则基于分解后的模态进行预测。数据集包含一个名为附件2.xlsx的数据文件。