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Taxi demand prediction based on LSTM with residuals and multi-head attention
Chih Jung Hsu,
Hung Hsuan Chen
資訊工程學系
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引文 斯高帕斯(Scopus)
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Keyphrases
Prediction Method
100%
Attention Mechanism
100%
Taxi Demand Prediction
100%
Multi-head Attention
100%
Deep Learning
66%
Taxi Demand
66%
Open Dataset
33%
Deep Learning Model
33%
Machine Learning Models
33%
XGBoost
33%
Gradient Boosting Decision Tree
33%
Time Series Model
33%
Autoregressive Integrated Moving Average (ARIMA)
33%
Library Learning
33%
Supervised Learning Model
33%
Prediction Challenge
33%
Residual Connection
33%
Ridge Regression
33%
Kaggle Competition
33%
LSTM Layer
33%
Computer Science
Long Short-Term Memory Network
100%
Attention (Machine Learning)
100%
Deep Learning Method
66%
Experimental Result
33%
Supervised Learning
33%
Deep Learning Model
33%
Open Source
33%
Extreme Gradient Boosting
33%
Ridge Regression
33%
Machine Learning
33%
Learning System
33%
Decision Tree
33%
LSTM Layer
33%
Engineering
Peak Hour
100%
Multi-Head Attention
100%
Deep Learning Method
75%
Experimental Result
25%
Learning System
25%
Lstm
25%
Economics, Econometrics and Finance
Time Series
100%
ARMA Model
100%
Machine Learning
100%
Chemical Engineering
Deep Learning Method
100%
Learning System
33%
Supervised Learning
33%
Psychology
Learning Model
100%
Autoregressive Integrated Moving Average
33%