Ensemble Learning for Short-Term Traffic Prediction Based on Gradient Boosting Machine
نویسندگان
چکیده
منابع مشابه
Machine Learning for Traffic Prediction
Using machine learning for predicting traffic is described in the context of a competition organized using the TunedIT platform. A heuristic is proposed for reconstructing the route of a car in a street graph from a temporal stream of its coordinates. A resilient propagation neural network for approximating the average velocity on a given street from irregular time series of instantaneous veloc...
متن کاملMachine Learning Techniques for Short-Term Electric Power Demand Prediction
Since several years ago, power consumption forecast has attracted considerable attention from the scientific community. Although there exist several works that deal with this issue, it remains open. The good management of energy consumption in HVAC (Heating, Ventilation and Air Conditioning) systems for large households and public buildings may benefit from a sustainable development in terms of...
متن کاملmachine learning for predictive management: short and long term prediction of phytoplankton biomass using genetic algorithm based recurrent neural networks
in the regulated nakdong river, algal proliferations are annually observed in some seasons, with cyanobacteria (microcystis aeruginosa) appearing in summer and diatom blooms (stephanodiscus hantzschii) in winter. this study aims to develop two ecological models forecasting future chlorophyll a at two time-steps (one-week and one-year forecasts), using recurrent neural networks tuned by genetic...
متن کاملA Hybrid Short-Term Traffic Flow Prediction Model Based on Singular Spectrum Analysis and Kernel Extreme Learning Machine
Short-term traffic flow prediction is one of the most important issues in the field of intelligent transport system (ITS). Because of the uncertainty and nonlinearity, short-term traffic flow prediction is a challenging task. In order to improve the accuracy of short-time traffic flow prediction, a hybrid model (SSA-KELM) is proposed based on singular spectrum analysis (SSA) and kernel extreme ...
متن کاملTraffic Prediction Based on Random Connectivity in Deep Learning with Long Short-Term Memory
Traffic prediction plays an important role in evaluating the performance of telecommunication networks and attracts intense research interests. A significant number of algorithms and models have been put forward to analyse traffic data and make prediction. In the recent big data era, deep learning has been exploited to mine the profound information hidden in the data. In particular, Long Short-...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Sensors
سال: 2017
ISSN: 1687-725X,1687-7268
DOI: 10.1155/2017/7074143