Surakhi et al., 2021 - Google Patents
Time-lag selection for time-series forecasting using neural network and heuristic algorithmSurakhi et al., 2021
View HTML- Document ID
- 13120043898090854336
- Author
- Surakhi O
- Zaidan M
- Fung P
- Hossein Motlagh N
- Serhan S
- AlKhanafseh M
- Ghoniem R
- Hussein T
- Publication year
- Publication venue
- Electronics
External Links
Snippet
The time-series forecasting is a vital area that motivates continuous investigate areas of intrigued for different applications. A critical step for the time-series forecasting is the right determination of the number of past observations (lags). This paper investigates the …
- 238000004422 calculation algorithm 0 title abstract description 42
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
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- 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
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
- G06F17/30587—Details of specialised database models
- G06F17/30595—Relational databases
- G06F17/30598—Clustering or classification
-
- 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
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/01—Social networking
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- 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
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F15/00—Digital computers in general; Data processing equipment in general
- G06F15/18—Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
- G06N7/005—Probabilistic networks
-
- 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
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
-
- 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
- G06Q30/00—Commerce, e.g. shopping or e-commerce
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Surakhi et al. | Time-lag selection for time-series forecasting using neural network and heuristic algorithm | |
Kontopoulou et al. | A review of ARIMA vs. machine learning approaches for time series forecasting in data driven networks | |
Adil et al. | LSTM and bat-based RUSBoost approach for electricity theft detection | |
Gul et al. | Detection of non-technical losses using SOSTLink and bidirectional gated recurrent unit to secure smart meters | |
Zahid et al. | Electricity price and load forecasting using enhanced convolutional neural network and enhanced support vector regression in smart grids | |
Li et al. | Hyperspectral classification based on texture feature enhancement and deep belief networks | |
Thi Kieu Tran et al. | Deep learning-based maximum temperature forecasting assisted with meta-learning for hyperparameter optimization | |
Srijiranon et al. | A hybrid framework using PCA, EMD and LSTM methods for stock market price prediction with sentiment analysis | |
Cheng et al. | Utilizing information bottleneck to evaluate the capability of deep neural networks for image classification | |
Shabani et al. | Gene expression programming coupled with unsupervised learning: a two-stage learning process in multi-scale, short-term water demand forecasts | |
Dinmohammadi et al. | Predicting energy consumption in residential buildings using advanced machine learning algorithms | |
Bellido-Jiménez et al. | Assessing machine learning models for gap filling daily rainfall series in a semiarid region of Spain | |
Murorunkwere et al. | Fraud detection using neural networks: A case study of income tax | |
Mao et al. | Robust detection of bearing early fault based on deep transfer learning | |
Surakhi et al. | An optimal stacked ensemble deep learning model for predicting time-series data using a genetic algorithm—an application for aerosol particle number concentrations | |
Hafezi et al. | Developing a data mining based model to extract predictor factors in energy systems: Application of global natural gas demand | |
Hu et al. | An innovative hourly water demand forecasting preprocessing framework with local outlier correction and adaptive decomposition techniques | |
Aljadani et al. | Mathematical modeling and analysis of credit scoring using the lime explainer: a comprehensive approach | |
Hussain et al. | A novel feature-engineered–NGBoost machine-learning framework for fraud detection in electric power consumption data | |
Si et al. | Cutting state diagnosis for shearer through the vibration of rocker transmission part with an improved probabilistic neural network | |
Qin et al. | A novel relational-based transductive transfer learning method for PolSAR images via time-series clustering | |
Liu et al. | A DLSTM-network-based approach for mechanical remaining useful life prediction | |
Jiang et al. | A Two-Stage Structural Damage Detection Method Based on 1D-CNN and SVM | |
Noh | Comparing the performance of corporate bankruptcy prediction models based on imbalanced financial data | |
Wang et al. | A Comparative Study of a Fully-Connected Artificial Neural Network and a Convolutional Neural Network in Predicting Bridge Maintenance Costs |