Sweeping-Based Temporal Aggregation

2017 ◽  
pp. 125-144 ◽  
Author(s):  
Danila Piatov ◽  
Sven Helmer
Keyword(s):  
Temporal Aggregation

Tail Risk Monotonicity Under Temporal Aggregation in GARCH(1,1) Models

2019 ◽  
Author(s):  
Paul Glasserman ◽  
Dan Pirjol ◽  
Qi Wu
Keyword(s):  
Temporal Aggregation ◽  
Tail Risk

scholarly journals Exploring the Effect of Temporal Aggregation on SCADA Data for Wind Turbine Prognosis Using a Normality Model

2021 ◽  
Vol 11 (14) ◽  
pp. 6405
Author(s):  
Pere Marti-Puig ◽  
Alejandro Bennásar-Sevillá ◽  
Alejandro Blanco-M. ◽  
Jordi Solé-Casals
Keyword(s):  
Wind Turbine ◽  
Low Cost ◽  
Wind Farms ◽  
Temporal Aggregation ◽  
Time Interval ◽  
Irreversible Loss ◽  
Loss Of Information ◽  
Statistical Measures ◽  
Maximum Value ◽  
Additional Equipment

Today, the use of SCADA data for predictive maintenance and forecasting of wind turbines in wind farms is gaining popularity due to the low cost of this solution compared to others that require the installation of additional equipment. SCADA data provides four statistical measures (mean, standard deviation, maximum value, and minimum value) of hundreds of wind turbine magnitudes, usually in a 5-min or 10-min interval. Several studies have analysed the loss of information associated with the reduction of information when using five minutes instead of four seconds as a sampling frequency, or when compressing a time series recorded at 5 min to 10 min, concluding that some, but not all, of these magnitudes are seriously affected. However, to our knowledge, there are no studies on increasing the time interval beyond 10 min to take these four statistical values, and how this aggregation affects prognosis models. Our work shows that, despite the irreversible loss of information that occurs in the first 5 min, increasing the time considered to take the four representative statistical values improves the performance of the predicted targets in normality models.

scholarly journals Recognizing the Aggregation Characteristics of Extreme Precipitation Events Using Spatio-Temporal Scanning and the Local Spatial Autocorrelation Model

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 218
Author(s):  
Changjun Wan ◽  
Changxiu Cheng ◽  
Sijing Ye ◽  
Shi Shen ◽  
Ting Zhang
Keyword(s):  
Spatial Autocorrelation ◽  
Extreme Precipitation ◽  
Eastern China ◽  
Temporal Aggregation ◽  
Global Changes ◽  
Percentile Method ◽  
Extreme Precipitation Events ◽  
Spatio Temporal ◽  
Precipitation Events ◽  
Local Spatial Autocorrelation

Precipitation is an essential climate variable in the hydrologic cycle. Its abnormal change would have a serious impact on the social economy, ecological development and life safety. In recent decades, many studies about extreme precipitation have been performed on spatio-temporal variation patterns under global changes; little research has been conducted on the regionality and persistence, which tend to be more destructive. This study defines extreme precipitation events by percentile method, then applies the spatio-temporal scanning model (STSM) and the local spatial autocorrelation model (LSAM) to explore the spatio-temporal aggregation characteristics of extreme precipitation, taking China in July as a case. The study result showed that the STSM with the LSAM can effectively detect the spatio-temporal accumulation areas. The extreme precipitation events of China in July 2016 have a significant spatio-temporal aggregation characteristic. From the spatial perspective, China’s summer extreme precipitation spatio-temporal clusters are mainly distributed in eastern China and northern China, such as Dongting Lake plain, the Circum-Bohai Sea region, Gansu, and Xinjiang. From the temporal perspective, the spatio-temporal clusters of extreme precipitation are mainly distributed in July, and its occurrence was delayed with an increase in latitude, except for in Xinjiang, where extreme precipitation events often take place earlier and persist longer.

Temporal Aggregation and Time Series

1982 ◽  
Vol 50 (3) ◽  
pp. 285 ◽  
Author(s):  
Bovas Abraham
Keyword(s):  
Time Series ◽  
Temporal Aggregation

Analyzing Travel Time Reliability from Sparse Probe Vehicle Data: A Case Study on the Effects of Spatial and Temporal Aggregation

2021 ◽  
pp. 036119812110315
Author(s):  
Markus Steinmaßl ◽  
Stefan Kranzinger ◽  
Karl Rehrl
Keyword(s):  
Travel Time ◽  
Time Of Day ◽  
Temporal Aggregation ◽  
Travel Time Reliability ◽  
Probe Vehicle ◽  
Vehicle Data ◽  
Depth Analysis ◽  
Data Source ◽  
Travel Time Index

Travel time reliability (TTR) indices have gained considerable attention for evaluating the quality of traffic infrastructure. Whereas TTR measures have been widely explored using data from stationary sensors with high penetration rates, there is a lack of research on calculating TTR from mobile sensors such as probe vehicle data (PVD) which is characterized by low penetration rates. PVD is a relevant data source for analyzing non-highway routes, as they are often not sufficiently covered by stationary sensors. The paper presents a methodology for analyzing TTR on (sub-)urban and rural routes with sparse PVD as the only data source that could be used by road authorities or traffic planners. Especially in the case of sparse data, spatial and temporal aggregations could have great impact, which are investigated on two levels: first, the width of time of day (TOD) intervals and second, the length of road segments. The spatial and temporal aggregation effects on travel time index (TTI) as prominent TTR measure are analyzed within an exemplary case study including three different routes. TTI patterns are calculated from data of one year grouped by different days-of-week (DOW) groups and the TOD. The case study shows that using well-chosen temporal and spatial aggregations, even with sparse PVD, an in-depth analysis of traffic patterns is possible.

Enabling spatio-temporal aggregation in Birds-Eye-View Vehicle Estimation

2021 ◽  
Author(s):  
Avishkar Saha ◽  
Oscar Mendez ◽  
Chris Russell ◽  
Richard Bowden
Keyword(s):  
Temporal Aggregation ◽  
Spatio Temporal

scholarly journals The impact of temporal aggregation on supply chains with ARMA(1,1) demand processes

2019 ◽  
Vol 273 (3) ◽  
pp. 920-932 ◽  
Author(s):  
Bahman Rostami-Tabar ◽  
M. Zied Babai ◽  
Mohammad Ali ◽  
John E. Boylan
Keyword(s):  
Supply Chains ◽  
Temporal Aggregation ◽  
The Impact ◽  
Demand Processes

Power Supply Scheduling Optimization from a Viewpoint of Spatio-Temporal Aggregation

2014 ◽  
Vol 47 (3) ◽  
pp. 4571-4577
Author(s):  
Tomonori Sadamoto ◽  
Ikuma Muto ◽  
Takayuki Ishizaki ◽  
Masakazu Koike ◽  
Jun-ichi Imura
Keyword(s):  
Power Supply ◽  
Temporal Aggregation ◽  
Scheduling Optimization ◽  
Spatio Temporal
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