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A Distance Correlation-Based Approach to Characterize the Effectiveness
  of Recurrent Neural Networks for Time Series Forecasting

A Distance Correlation-Based Approach to Characterize the Effectiveness of Recurrent Neural Networks for Time Series Forecasting

28 July 2023
Christopher Salazar
A. Banerjee
    AI4TS
ArXivPDFHTML

Papers citing "A Distance Correlation-Based Approach to Characterize the Effectiveness of Recurrent Neural Networks for Time Series Forecasting"

3 / 3 papers shown
Title
Informer: Beyond Efficient Transformer for Long Sequence Time-Series
  Forecasting
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
Haoyi Zhou
Shanghang Zhang
J. Peng
Shuai Zhang
Jianxin Li
Hui Xiong
Wan Zhang
AI4TS
169
3,876
0
14 Dec 2020
Benefits of depth in neural networks
Benefits of depth in neural networks
Matus Telgarsky
136
602
0
14 Feb 2016
Measuring and testing dependence by correlation of distances
Measuring and testing dependence by correlation of distances
G. Székely
Maria L. Rizzo
N. K. Bakirov
177
2,577
0
28 Mar 2008
1