most citedA Novel Hybrid Framework for Hourly PM2.5 Concentration Forecasting Using CEEMDAN and Deep Temporal Convolutional Neural Network

5 citations · 5 across the 2 of their papers we have counts for

collaborators

7 papers

eess.SP20205 cited

A Novel Hybrid Framework for Hourly PM2.5 Concentration Forecasting Using CEEMDAN and Deep Temporal Convolutional Neural Network

Fuxin Jiang, Chengyuan Zhang, Shaolong Sun +1

For hourly PM2.5 concentration prediction, accurately capturing the data patterns of external factors that affect PM2.5 concentration changes, and constructing a forecasting model…

physics.soc-ph2020

Knowledge Mapping in Electricity Demand Forecasting: A Scientometric Insight

Dongchuan Yang, Ju-e Guo, Jie Li +2

Forecasting electricity demand plays a fundamental role in the operation and planning procedures of power systems and the publications about electricity demand forecasting increasi…

cs.DL2020

New Research Trends in Unconventional Oil and Gas Environmental Issue: A Bibliometric Analysis

Dan Bi, Ju-e Guo, Shouyang Wang +1

With the booming of unconventional gas production in the world, how to balance environment pollution risk and economy of unconventional gas have become a common dilemma around the…

stat.AP2020

Seasonal and Trend Forecasting of Tourist Arrivals: An Adaptive Multiscale Ensemble Learning Approach

Shaolong Suna, Dan Bi, Ju-e Guo +1

The accurate seasonal and trend forecasting of tourist arrivals is a very challenging task. In the view of the importance of seasonal and trend forecasting of tourist arrivals, and…

cs.AI2020

AdaEnsemble Learning Approach for Metro Passenger Flow Forecasting

Shaolong Sun, Dongchuan Yang, Ju-e Guo +1

Accurate and timely metro passenger flow forecasting is critical for the successful deployment of intelligent transportation systems. However, it is quite challenging to propose an…

econ.GN2020

A New Decomposition Ensemble Approach for Tourism Demand Forecasting: Evidence from Major Source Countries

Chengyuan Zhang, Fuxin Jiang, Shouyang Wang +1

The Asian-pacific region is the major international tourism demand market in the world, and its tourism demand is deeply affected by various factors. Previous studies have shown th…