arXiv:2003.05672
Time Series Forecasting Using LSTM Networks: A Symbolic Approach
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Revisiting LLMs as Zero-Shot Time-Series Forecasters: Small Noise Can Break Large Models
Trojan Horse Hunt in Time Series Forecasting for Space Operations
Temporal Convolutional Attention Neural Networks for Time Series Forecasting
Interpretable Multivariate Time Series Forecasting with Temporal Attention Convolutional Neural Networks
DATE: Dual Attentive Tree-aware Embedding for Customs Fraud Detection
A Combination Method for Android Malware Detection Based on Control Flow Graphs and Machine Learning Algorithms
토론
이 논문과 연결된 포럼 글이 아직 없습니다.