Deformation Prediction Based on Time Series Analysis and Grey System Theory

2011 ◽  
Vol 368-373 ◽  
pp. 2147-2152 ◽  
Author(s):  
Dong Liang Qiao ◽  
Ming Zhao
Keyword(s):  
System Theory ◽  
Time Series ◽  
Time Series Analysis ◽  
Large Deviation ◽  
Original Data ◽  
Grey System Theory ◽  
Grey System ◽  
Deformation Prediction ◽  
Series Analysis

For the long-term monitoring of structure, the deformation trend changes periodically and is hard to extract. A small amount of recent data can be selected to avoid such problem. The study refers to the idea of grey system theory and provides an improved way of deformation prediction in time series analysis with a small amount of data. By cumulating the original data, the trend item is made clear and the rule of data becomes obvious. The prediction results show that the way provided by this article gives a more accurate prediction in the short term. When the prediction results have a large deviation with actual deformation, it can be believed that the trend has changed and the monitored structure may be affected.

scholarly journals Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1151
Author(s):  
Carolina Gijón ◽  
Matías Toril ◽  
Salvador Luna-Ramírez ◽  
María Luisa Marí-Altozano ◽  
José María Ruiz-Avilés
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Data Traffic ◽  
Medium Term ◽  
Short Term ◽  
Traffic Forecasting ◽  
Series Analysis ◽  
Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

Assessment of Long-Term Sensor Radiometric Degradation Using Time Series Analysis

2014 ◽  
Vol 52 (5) ◽  
pp. 2960-2976 ◽  
Author(s):  
Wonkook Kim ◽  
Tao He ◽  
Dongdong Wang ◽  
Changyong Cao ◽  
Shunlin Liang
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Series Analysis

Time series analysis of microbiome and metabolome at multiple body sites in steady long-term isolation confinement

Gut ◽  
2020 ◽  
pp. gutjnl-2020-320666
Author(s):  
Qiang Feng ◽  
Xiang Lan ◽  
Xiaoli Ji ◽  
Meihui Li ◽  
Shili Liu ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Series Analysis

scholarly journals Time-series analysis of abundance indices of young fish

2004 ◽  
Vol 61 (2) ◽  
pp. 176-183 ◽  
Author(s):  
G. Gudmundsson
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Large Deviation ◽  
Natural Mortality ◽  
Cohort Size ◽  
Young Fish ◽  
Stock Size ◽  
Series Analysis ◽  
Abundance Indices ◽  
The Relationship

Abstract Catch-at-age analysis provides estimates of stock size at ages when the fish have reached catchable size. Survey indices contain information about relative cohort size at younger ages. The present analysis is concerned with survey indices of juveniles up to the youngest age where stock estimates, based on time-series analysis of catch-at-age data, are available. A stock estimate at that age from catch-at-age data is also included. A common model of the relationship between stock size and survey indices is combined with the model describing the decline of a stock by natural mortality. Random variations in natural mortality are defined separately from sampling variations and irregular catchability in the survey. The stock size and magnitudes of the random variations are estimated by the Kalman filter, which also provides predictions of future recruitment to the catchable stock. Analysis of observations of Icelandic cod reveals a large deviation from proportionality in the relationship between the index and the stock estimates in the youngest ages, but haddock data are compatible with proportionality. Variations in natural mortality during the second to fourth year of cod and the second to third year of haddock are not a major factor in variations of stock size.

Time series analysis of long-term data sets of atmospheric mercury concentrations

2004 ◽  
Vol 380 (3) ◽  
pp. 493-501 ◽  
Author(s):  
Christian Temme ◽  
Ralf Ebinghaus ◽  
J�rgen W. Einax ◽  
Alexandra Steffen ◽  
William H. Schroeder
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Atmospheric Mercury ◽  
Data Sets ◽  
Series Analysis ◽  
Term Data
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