论文标题
时间序列使用LSTM网络预测：符号方法
Time Series Forecasting Using LSTM Networks: A Symbolic Approach
论文作者
论文摘要
在原始数值时间序列数据上训练的机器学习方法表现出基本限制，例如对超级参数的高灵敏度，甚至对随机重量的初始化。提出并应用了与减小符号表示的复发神经网络与降低尺寸符号表示形式的组合，以预测时间序列。结果表明，符号表示可以帮助减轻上述问题，此外，还可以在不牺牲预测绩效的情况下进行更快的培训。
Machine learning methods trained on raw numerical time series data exhibit fundamental limitations such as a high sensitivity to the hyper parameters and even to the initialization of random weights. A combination of a recurrent neural network with a dimension-reducing symbolic representation is proposed and applied for the purpose of time series forecasting. It is shown that the symbolic representation can help to alleviate some of the aforementioned problems and, in addition, might allow for faster training without sacrificing the forecast performance.
