Miao et al., 2016 - Google Patents
MSFS: multiple spatio-temporal scales traffic forecasting in mobile cellular networkMiao et al., 2016
- Document ID
- 4117673368316463643
- Author
- Miao D
- Sun W
- Qin X
- Wang W
- Publication year
- Publication venue
- 2016 IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech)
External Links
Snippet
With peta-bytes of data that are continuously collected about various aspects of the mobile networks, one of the main challenges when dealing with such data is performing accurate predictions in order to address a broad class of application problems, ranging from mobile …
- 230000001413 cellular 0 title description 21
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
- G06Q10/063—Operations research or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Sayed et al. | Deep and transfer learning for building occupancy detection: A review and comparative analysis | |
Nikravesh et al. | Mobile network traffic prediction using MLP, MLPWD, and SVM | |
Bassamzadeh et al. | Multiscale stochastic prediction of electricity demand in smart grids using Bayesian networks | |
Li et al. | Predicting Short‐Term Electricity Demand by Combining the Advantages of ARMA and XGBoost in Fog Computing Environment | |
Jiang et al. | Outlier detection approaches based on machine learning in the internet-of-things | |
Al-Jarrah et al. | Multi-layered clustering for power consumption profiling in smart grids | |
Nichiforov et al. | Deep learning techniques for load forecasting in large commercial buildings | |
He et al. | Graph attention spatial-temporal network for deep learning based mobile traffic prediction | |
Fernandez-Basso et al. | A fuzzy mining approach for energy efficiency in a Big Data framework | |
Goyal et al. | Exploratory analysis of machine learning techniques to predict energy efficiency in buildings | |
Tornai et al. | Recurrent neural network based user classification for smart grids | |
Miao et al. | MSFS: multiple spatio-temporal scales traffic forecasting in mobile cellular network | |
CN112801411A (en) | Network flow prediction method based on generation countermeasure network | |
Wang | Construction and simulation of performance evaluation index system of Internet of Things based on cloud model | |
Cerquitelli | Predicting large scale fine grain energy consumption | |
Liu et al. | A SDN-based intelligent prediction approach to power traffic identification and monitoring for smart network access | |
Li et al. | Large-scale comparison and demonstration of continual learning for adaptive data-driven building energy prediction | |
Balaji et al. | Deep Learning Based Energy Consumption Prediction on Internet of Things Environment. | |
Nichiforov et al. | Efficient Load Forecasting Model Assessment for Embedded Building Energy Management Systems | |
Nichiforov et al. | Learning dominant usage from anomaly patterns in building energy traces | |
Wang et al. | A model of telecommunication network performance anomaly detection based on service features clustering | |
Wu et al. | A prediction model based on time series data in Intelligent Transportation System | |
Singh et al. | Grey Wolf Optimization Based CNN-LSTM Network for the Prediction of Energy Consumption in Smart Home Environment | |
Mohammadi et al. | Exploiting the spatio-temporal patterns in IoT data to establish a dynamic ensemble of distributed learners | |
Li et al. | Power Load Curve Clustering based on ISODATA |