Data-Based Modeling Approaches for Short-Term Prediction of Embankment Settlement Using Magnetic Extensometer Time-Series Data

2022 ◽  
Vol 22 (2) ◽  
Author(s):  
Faisal Siddiqui ◽  
Paul Sargent ◽  
Gary Montague
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Short Term ◽  
Modeling Approaches ◽  
Term Prediction ◽  
Short Term Prediction

Chaotic Short-Term Prediction to Water Flow into Hydroelectric Power Stations

1998 ◽  
Vol 10 (4) ◽  
pp. 301-304
Author(s):  
Masaya Koyama ◽  
◽  
Tadashi lokibe
Keyword(s):  
Mathematical Models ◽  
Water Flow ◽  
Time Series Data ◽  
Deterministic Chaos ◽  
Series Data ◽  
Hydroelectric Power ◽  
Short Term ◽  
Power Stations ◽  
Term Prediction ◽  
Short Term Prediction

We applied local fuzzy reconstruction as deterministic nonlinear short-term prediction to data for water flow into hydroelectric power stations. Such prediction involves complex natural phenomena, and conventional hydraulics-based mathematical models do not produce satisfactory results. When a neural network is used, its construction cannot be easily determined, so extra neural networks must also be provided separately, based on experts' opinions. To solve these problems, we held that if time-series data of the inflow rate for hydroelectric power stations exhibits deterministic chaos, the status in the near future is predicted. Typical outflow analysis using conventional mathematical models is described briefly, followed by local fuzzy reconstruction, then results are given from applying this to water flow prediction.

scholarly journals Analysis and Prediction of Alerts in Perimeter Intrusion Detection System

2020 ◽  
Vol 70 (6) ◽  
pp. 619-625
Author(s):  
Rizul Aggarwal ◽  
Anjali Goswami ◽  
Jitender Kumar ◽  
Gwyneth Abdiel Chullai
Keyword(s):  
Time Series ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Time Series Data ◽  
Detection System ◽  
Series Data ◽  
Support Vector ◽  
Short Term ◽  
Term Prediction

Perimeter surveillance systems play an important role in the safety and security of the armed forces. These systems tend to generate alerts in advent of anomalous situations, which require human intervention. The challenge is the generation of false alerts or alert flooding which makes these systems inefficient. In this paper, we focus on short-term as well as long-term prediction of alerts in the perimeter intrusion detection system. We have explored the dependent and independent aspects of the alert data generated over a period of time. Short-term prediction is realized by exploiting the independent aspect of data by narrowing it down to a time-series problem. Time-series analysis is performed by extracting the statistical information from the historical alert data. A dual-stage approach is employed for analyzing the time-series data and support vector regression is used as the regression technique. It is helpful to predict the number of alerts for the nth hour. Additionally, to understand the dependent aspect, we have investigated that the deployment environment has an impact on the alerts generated. Long-term predictions are made by extracting the features based on the deployment environment and training the dataset using different regression models. Also, we have compared the predicted and expected alerts to recognize anomalous behaviour. This will help in realizing the situations of alert flooding over the potential threat.

scholarly journals Track Irregularity Time Series Analysis and Trend Forecasting

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Jia Chaolong ◽  
Xu Weixiang ◽  
Wang Futian ◽  
Wang Hanning
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Short Term ◽  
Measuring Point ◽  
Trend Forecasting ◽  
Changing Trend ◽  
Track Irregularity ◽  
Linear Differential

The combination of linear and nonlinear methods is widely used in the prediction of time series data. This paper analyzes track irregularity time series data by using gray incidence degree models and methods of data transformation, trying to find the connotative relationship between the time series data. In this paper, GM(1,1)is based on first-order, single variable linear differential equations; after an adaptive improvement and error correction, it is used to predict the long-term changing trend of track irregularity at a fixed measuring point; the stochastic linear AR, Kalman filtering model, and artificial neural network model are applied to predict the short-term changing trend of track irregularity at unit section. Both long-term and short-term changes prove that the model is effective and can achieve the expected accuracy.

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