Real time prediction method of sensor output time series

2009 ◽  
Author(s):  
Zhicheng Liu ◽  
Xudong Pei ◽  
Gang Huang
Keyword(s):  
Time Series ◽  
Real Time ◽  
Prediction Method ◽  
Sensor Output ◽  
Time Prediction ◽  
Output Time

Real Time Prediction Method of Sensor Output Time Series

2014 ◽  
Vol 912-914 ◽  
pp. 1322-1326
Author(s):  
Zhi Cheng Liu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Real Time ◽  
Simulation Experiment ◽  
Prediction Method ◽  
Sensor Output ◽  
Wavelet Filtering ◽  
Time Prediction ◽  
Network Method ◽  
Output Time

In order to improve the real time prediction precision of sensor output time series, the predictable inner mechanism of time series is analyzed, and a method using wavelet filtering and neural network is proposed. Sensor output time series are first handled with wavelet filtering, and then predicted by neural network method. The proposed method can eliminate effect of measurement noise on prediction precision. Simulation experiment shows a higher prediction precision by the method. A new idea is given to increase prediction precision of sensor output time series by neural network-based methods.

Forecasting Model of Short-Term Traffic Flow Based on Cloud Model

2011 ◽  
Vol 94-96 ◽  
pp. 38-42
Author(s):  
Qin Liu ◽  
Jian Min Xu
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Cloud Model ◽  
Prediction Method ◽  
Absolute Error ◽  
Short Term ◽  
Guangzhou City ◽  
Time Prediction ◽  
Real Time Traffic ◽  
Absolute Percent Error

In order to improve the prediction precision of the short-term traffic flow, a prediction method of short-term traffic flow based on cloud model was proposed. The traffic flow was fit by cloud model. The history cloud and the present cloud were built by historical traffic flow and present traffic flow. The forecast cloud is produced by both clouds. Then, combining with the volume of the short-term traffic flow of an intersection in Guangzhou City, the model was calculated and simulated through programming. Max Absolute Error (MAE) and Mean Absolute percent Error (MAPE) were used to estimate the effect of prediction. The simulation results indicate that this prediction method is effective and advanced. The change of the historical and real time traffic flow is taken into account in this method. Because the short-term traffic flow is dealt with as a whole, the error of prediction is avoided. The prediction precision and real-time prediction are satisfied.

Real-Time Prediction Method for Performance Degradation Trend Based on Reliability Experimental Data

2013 ◽  
Vol 321-324 ◽  
pp. 757-761 ◽  
Author(s):  
Chen Liang Song ◽  
Zhen Liu ◽  
Bin Long ◽  
Cheng Lin Yang
Keyword(s):  
Experimental Data ◽  
Real Time ◽  
Field Data ◽  
Prediction Method ◽  
Field Method ◽  
Performance Degradation ◽  
New Method ◽  
Time Prediction ◽  
Prediction Result ◽  
The Relationship

According to the real-time prediction for performance degradation trend, the commonly used method is just based on field data. But this methods prediction result will not be so much ideal when the fitting of degradation trend of field data is not good. To solve the problem, the paper introduces a new method which is not only based on field method but also based on reliability experimental data coming from the history experiment. We use the relationship between the field data and reliability experimental data to get the result of the two kinds of data respectively and then get the weights according to the two prediction results. Finally, the final real-time prediction result for performance degradation tendency can obtain by allocating the weights to the two prediction results.

Near real-time prediction of U.S. corn yields based on time-series MODIS data

2014 ◽  
Vol 147 ◽  
pp. 219-231 ◽  
Author(s):  
Toshihiro Sakamoto ◽  
Anatoly A. Gitelson ◽  
Timothy J. Arkebauer
Keyword(s):  
Time Series ◽  
Real Time ◽  
Modis Data ◽  
Time Prediction

scholarly journals COVIDSpread: real-time prediction of COVID-19 spread based on time-series modelling

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1110
Author(s):  
Siroos Shahriari ◽  
Taha Hossein Rashidi ◽  
AKM Azad ◽  
Fatemeh Vafaee
Keyword(s):  
Time Series ◽  
Real Time ◽  
Time Window ◽  
Time Series Models ◽  
Data Streaming ◽  
Time Data ◽  
Time Prediction ◽  
Time Series Modelling ◽  
Short Run ◽  
Modelling Techniques

A substantial amount of data about the COVID-19 pandemic is generated every day. Yet, data streaming, while considerably visualized, is not accompanied with modelling techniques to provide real-time insights. This study introduces a unified platform, COVIDSpread, which integrates visualization capabilities with advanced statistical methods for predicting the virus spread in the short run, using real-time data. The platform uses time series models to capture any possible non-linearity in the data. COVIDSpread enables lay users, and experts, to examine the data and develop several customized models with different restrictions such as models developed for a specific time window of the data. COVIDSpread is available here: http://vafaeelab.com/COVID19TS.html.

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