跳至主導覽
跳至搜尋
跳過主要內容
國立中央大學 首頁
說明與常見問題
English
中文
首頁
人才檔案
研究單位
研究計畫
研究成果
資料集
榮譽/獲獎
學術活動
新聞/媒體
影響
按專業知識、姓名或所屬機構搜尋
Taxi demand prediction based on LSTM with residuals and multi-head attention
Chih Jung Hsu,
Hung Hsuan Chen
資訊工程學系
研究成果
:
書貢獻/報告類型
›
會議論文篇章
›
同行評審
4
引文 斯高帕斯（Scopus）
總覽
指紋
研究計畫
(1)
指紋
深入研究「Taxi demand prediction based on LSTM with residuals and multi-head attention」主題。共同形成了獨特的指紋。
排序方式
重量
按字母排序
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%