scholarly journals Passenger Flow Forecasting in Metro Transfer Station Based on the Combination of Singular Spectrum Analysis and AdaBoost-Weighted Extreme Learning Machine

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3555
Author(s):  
Wei Zhou ◽  
Wei Wang ◽  
De Zhao
Keyword(s):  
Extreme Learning Machine ◽  
Spectrum Analysis ◽  
Short Term Memory ◽  
Singular Spectrum Analysis ◽  
Intelligent Transport System ◽  
Training Time ◽  
Passenger Flow ◽  
Singular Spectrum ◽  
Weighted Extreme Learning Machine ◽  
Learning Machine

The metro system plays an important role in urban public transit, and the passenger flow forecasting is fundamental to assisting operators establishing an intelligent transport system (ITS). The forecasting results can provide necessary information for travelling decision of travelers and metro operations of managers. In order to investigate the inner characteristics of passenger flow and make a more accurate prediction with less training time, a novel model (i.e., SSA-AWELM), a combination of singular spectrum analysis (SSA) and AdaBoost-weighted extreme learning machine (AWELM), is proposed in this paper. SSA is developed to decompose the original data into three components of trend, periodicity, and residue. AWELM is developed to forecast each component desperately. The three predicted results are summed as the final outcomes. In the experiments, the dataset is collected from the automatic fare collection (AFC) system of Hangzhou metro in China. We extracted three weeks of passenger flow to carry out multistep prediction tests and a comparison analysis. The results indicate that the proposed SSA-AWELM model can reduce both predicted errors and training time. In particular, compared with the prevalent deep-learning model long short-term memory (LSTM) neural network, SSA-AWELM has reduced the testing errors by 22% and saved time by 84%, on average. It demonstrates that SSA-AWELM is a promising approach for passenger flow forecasting.

scholarly journals An Application of Hybrid Forecasting Singular Spectrum Analysis – Extreme Learning Machine Method in Foreign Tourists Forecasting

2019 ◽  
Vol 5 (2) ◽  
pp. 60-68
Author(s):  
Muhammad Fajar
Keyword(s):  
Extreme Learning Machine ◽  
Spectrum Analysis ◽  
Input Parameter ◽  
Singular Spectrum Analysis ◽  
Tourism Development ◽  
Machine Method ◽  
Singular Spectrum ◽  
Business Opportunities ◽  
Hybrid Forecasting ◽  
Learning Machine

International tourism is one indicator of measuring tourism development. Tourism development is important for the national economy since tourism could boost foreign exchange, create business opportunities, and provide employment opportunities. The prediction of foreign tourist numbers in the future obtained from forecasting is used as an input parameter for strategy and tourism programs planning. In this paper, the Hybrid Singular Spectrum Analysis – Extreme Learning Machine (SSA-ELM) is used to forecast the number of foreign tourists.  Data used is the number of foreign tourists January 1980 - December 2017 taken from Badan Pusat Statistik (Statistics Indonesia). The result of this research concludes that Hybrid SSA-ELM performance is very good at forecasting the number of foreign tourists. It is shown by the MAPE value of 4.91 percent with eight observations out a sample.

scholarly journals Short-Term Traffic Flow Prediction of Expressway: A Hybrid Method Based on Singular Spectrum Analysis Decomposition

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Chunyan Shuai ◽  
Zhengyang Pan ◽  
Lun Gao ◽  
HongWu Zuo
Keyword(s):  
Traffic Flow ◽  
Hybrid Model ◽  
Spectrum Analysis ◽  
Short Term Memory ◽  
Singular Spectrum Analysis ◽  
Support Vector ◽  
Short Term ◽  
Traffic Flow Prediction ◽  
Singular Spectrum ◽  
Flow Prediction

Real-time expressway traffic flow prediction is always an important research field of intelligent transportation, which is conducive to inducing and managing traffic flow in case of congestion. According to the characteristics of the traffic flow, this paper proposes a hybrid model, SSA-LSTM-SVR, to improve forecasting accuracy of the short-term traffic flow. Singular Spectrum Analysis (SSA) decomposes the traffic flow into one principle component and three random components, and then in terms of different characteristics of these components, Long Short-Term Memory (LSTM) and Support Vector Regression (SVR) are applied to make prediction of different components, respectively. By fusing respective forecast results, SSA-LSTM-SVR obtains the final short-term predictive value. Experiments on the traffic flows of Guizhou expressway in January 2016 show that the proposed SSA-LSTM-SVR model has lower predictive errors and a higher accuracy and fitting goodness than other baselines. This illustrates that a hybrid model for traffic flow prediction based on components decomposition is more effective than a single model, since it can capture the main regularity and random variations of traffic flow.

scholarly journals Short-Term Net Load Forecasting with Singular Spectrum Analysis and LSTM Neural Networks

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4107
Author(s):  
Akylas Stratigakos ◽  
Athanasios Bachoumis ◽  
Vasiliki Vita ◽  
Elias Zafiropoulos
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Spectrum Analysis ◽  
Short Term Memory ◽  
Predictive Accuracy ◽  
Singular Spectrum Analysis ◽  
Hybrid Approach ◽  
Load Forecasting ◽  
Short Term ◽  
Singular Spectrum

Short-term electricity load forecasting is key to the safe, reliable, and economical operation of power systems. An important challenge that arises with high-frequency load series, e.g., hourly load, is how to deal with the complex seasonal patterns that are present. Standard approaches suggest either removing seasonality prior to modeling or applying time series decomposition. This work proposes a hybrid approach that combines Singular Spectrum Analysis (SSA)-based decomposition and Artificial Neural Networks (ANNs) for day-ahead hourly load forecasting. First, the trajectory matrix of the time series is constructed and decomposed into trend, oscillating, and noise components. Next, the extracted components are employed as exogenous regressors in a global forecasting model, comprising either a Multilayer Perceptron (MLP) or a Long Short-Term Memory (LSTM) predictive layer. The model is further extended to include exogenous features, e.g., weather forecasts, transformed via parallel dense layers. The predictive performance is evaluated on two real-world datasets, controlling for the effect of exogenous features on predictive accuracy. The results showcase that the decomposition step improves the relative performance for ANN models, with the combination of LSTM and SAA providing the best overall performance.

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