Data Mining Pt. Reyes Buoy for Rare Wave Groups

2015 ◽  
Vol 138 (1) ◽  
Author(s):  
Harleigh C. Seyffert ◽  
Armin W. Troesch
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
University Of California ◽  
Series Data ◽  
Wave Groups ◽  
Naturally Occurring ◽  
Wave Elevation ◽  
Information Program ◽  
Elevation Data ◽  
Definition Of

This paper addresses the existence of rare wave groups by examining time series data from the Pt. Reyes buoy. The buoy is operated by the Coastal Data Information Program (CDIP), University of California San Diego. The definition of rare wave groups, as defined by Kim and Troesch, used in this paper differs from the more commonly used wave group definition based on threshold crossings. With the time series data from the Pt. Reyes buoy, these rare wave groups are shown to be a naturally occurring phenomenon. The essential features of the data are examined, as well as the analysis methods and findings. By sifting through 17 years of wave elevation data from the Pt. Reyes buoy, this preliminary work addresses not only the question to what extent rare wave groups exist in nature but also what their probability of occurrence is.

Data Mining Pt. Reyes Buoy for Rare Wave Groups

2015 ◽  
Author(s):  
Harleigh C. Seyffert ◽  
Armin W. Troesch
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
University Of California ◽  
Series Data ◽  
Wave Groups ◽  
Naturally Occurring ◽  
Wave Elevation ◽  
Information Program ◽  
Elevation Data ◽  
Definition Of

This paper addresses the existence of rare wave groups, as defined by Kim and Troesch [1], by examining time series data from the Pt. Reyes buoy. The buoy is operated by the Coastal Data Information Program (CDIP), University of California San Diego. The definition of rare wave groups [1] used in this paper differs from the more commonly used wave group definition based on threshold crossings. With the time series data from the Pt. Reyes buoy, these rare wave groups are shown to be a naturally occurring phenomenon. The nature of the data is examined, as well as the analysis methods and findings. By sifting through 17 years of wave elevation data from the Pt. Reyes buoy, this preliminary work addresses not only the question to what extent rare wave groups exist in nature, but also, what their probability of occurrence is.

Efficient Time Series Clustering and Its Application to Social Network Mining

2014 ◽  
Vol 23 (2) ◽  
pp. 213-229 ◽  
Author(s):  
Cangqi Zhou ◽  
Qianchuan Zhao
Keyword(s):  
Time Series ◽  
Social Network ◽  
Information Diffusion ◽  
Time Series Data ◽  
Online Social Network ◽  
Dissimilarity Measure ◽  
Series Data ◽  
Formation Mechanisms ◽  
Network Analyses ◽  
Definition Of

AbstractMining time series data is of great significance in various areas. To efficiently find representative patterns in these data, this article focuses on the definition of a valid dissimilarity measure and the acceleration of partitioning clustering, a common group of techniques used to discover typical shapes of time series. Dissimilarity measure is a crucial component in clustering. It is required, by some particular applications, to be invariant to specific transformations. The rationale for using the angle between two time series to define a dissimilarity is analyzed. Moreover, our proposed measure satisfies the triangle inequality with specific restrictions. This property can be employed to accelerate clustering. An integrated algorithm is proposed. The experiments show that angle-based dissimilarity captures the essence of time series patterns that are invariant to amplitude scaling. In addition, the accelerated algorithm outperforms the standard one as redundancies are pruned. Our approach has been applied to discover typical patterns of information diffusion in an online social network. Analyses revealed the formation mechanisms of different patterns.

Intercomparison of Seastate and Zerocrossing Parameters From the WACSIS Field Experiment and Interpretation Using Video Evidence

2002 ◽  
Author(s):  
Stephen F. Barstow ◽  
Harald E. Krogstad ◽  
Lasse Lo̸nseth ◽  
Jan Petter Mathisen ◽  
Gunnar Mo̸rk ◽  
...  
Keyword(s):  
Time Series ◽  
The Netherlands ◽  
Field Experiment ◽  
Time Series Data ◽  
Series Data ◽  
Sea State ◽  
Single Wave ◽  
Video Recordings ◽  
Wave Parameters ◽  
Wave Elevation

During the WACSIS field experiment, wave elevation time series data were collected over the period December 1997 to May 1998 on and near the Meetpost Nordwijk platform off the coast of the Netherlands from an EMI laser, a Saab radar, a Baylor Wave Staff, a Vlissingen step gauge, a Marex radar and a Directional Waverider. This paper reports and interprets, with the help of simultaneous dual video recordings of the ocean surface, an intercomparison of both single wave and sea state wave parameters.

Testing for Instrumentation in Transportation Time Series Data: A Case Study

1997 ◽  
Vol 1581 (1) ◽  
pp. 89-92
Author(s):  
Steven M. Rock
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
Monthly Data ◽  
Autoregressive Integrated Moving Average ◽  
Definition Of ◽  
Accident Statistics ◽  
Percent Decline

Instrumentation is one of the threats to the validity of experiments. Four possible cases of instrumentation in a time series of traffic accident statistics in Illinois since the mid-1970s were tested, primarily by using autoregressive integrated moving average methods. Two of these cases, a 1977 change in the reporting threshold for property-damage-only (PDO) accidents and a 1989 change in the definition of a fatality, were not found to be significant. A 1989 change in the method of tabulating monthly data and a 1992 change in the reporting threshold for PDO accidents were statistically significant. These two cases combined could account for a more than 15 percent decline in PDO accidents.

scholarly journals Time Series Chains: A Novel Tool for Time Series Data Mining

2018 ◽  
Author(s):  
Yan Zhu ◽  
Makoto Imamura ◽  
Daniel Nikovski ◽  
Eamonn Keogh
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Time Series Data ◽  
Ordered Set ◽  
Series Data ◽  
Massive Datasets ◽  
Scalable Algorithm ◽  
A Chain ◽  
Definition Of ◽  
Difference Time

Since their introduction over a decade ago, time se-ries motifs have become a fundamental tool for time series analytics, finding diverse uses in dozens of domains. In this work we introduce Time Series Chains, which are related to, but distinct from, time series motifs. Informally, time series chains are a temporally ordered set of subsequence patterns, such that each pattern is similar to the pattern that preceded it, but the first and last patterns are arbi-trarily dissimilar. In the discrete space, this is simi-lar to extracting the text chain “hit, hot, dot, dog” from a paragraph. The first and last words have nothing in common, yet they are connected by a chain of words with a small mutual difference. Time Series Chains can capture the evolution of systems, and help predict the future. As such, they potentially have implications for prognostics. In this work, we introduce a robust definition of time series chains, and a scalable algorithm that allows us to discover them in massive datasets.

Intercomparison of Sea-State and Zero-Crossing Parameters From the WACSIS Field Experiment and Interpretation Using Video Evidence

2004 ◽  
Vol 126 (1) ◽  
pp. 35-42 ◽  
Author(s):  
Stephen F. Barstow, ◽  
Harald E. Krogstad ◽  
Lasse Lønseth ◽  
Jan Petter Mathisen ◽  
Gunnar Mørk ◽  
...  
Keyword(s):  
Time Series ◽  
Field Experiment ◽  
Time Series Data ◽  
Series Data ◽  
Sea State ◽  
Single Wave ◽  
Zero Crossing ◽  
Video Recordings ◽  
Wave Parameters ◽  
Wave Elevation

During the WACSIS field experiment, wave elevation time series data were collected over the period December 1997 to May 1998 on and near the Meetpost Nordwijk platform off the coast of the Netherlands from an EMI laser, a Saab radar, a Baylor Wave Staff, a Vlissingen step gauge, a Marex radar and a Directional Waverider. This paper reports and interprets, with the help of simultaneous dual video recordings of the ocean surface, an intercomparison of both single wave and sea state wave parameters.

scholarly journals Evaluation of Time Series TanDEM-X Digital Elevation Models

2014 ◽  
Vol XL-8 ◽  
pp. 437-441 ◽  
Author(s):  
M. Jain ◽  
R. Deo ◽  
V. Kumar ◽  
Y. S. Rao
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Time Series Data ◽  
Incidence Angle ◽  
Weighted Average ◽  
Sar Interferometry ◽  
Series Data ◽  
Generation Process ◽  
Digital Elevation ◽  
Elevation Data

Digital Elevation Model (DEM) is an important input for geo-spatial analysis. For various applications like flood management, ortho rectification of remote sensing images, navigation, architectural works, defence, etc., high resolution DEM is required. TanDEM-X mission was launched in 2010 to obtain high resolution global DEM with HTRI-3 standard. SAR interferometry (InSAR) technique is used for DEM generation from TanDEM-X SAR data. The accuracy of DEM depends on many parameters like height ambiguity, incidence angle, polarization, etc. In this study, time series TanDEM-X data spanning over 3 years, had processed for generating DEM at the spatial resolution of 6 m and their accuracy had studied using DGPS elevation data and SRTM 90 m DEM. The products generated during DEM generation process are DEM, precision (or height error), coherence, layover and shadow images. Using weighted average fusion technique, ascending and descending DEMs are fused for improving the quality of DEM and to reduce invalid pixels corresponding to layover and shadow areas. Results from time series data were analysed and found RMSE error of fused DEMs is in the range of 2 m to 4 m, while individual DEM has accuracy of 3 m to 6 m with respect to DGPS elevation data. Fused DEMs are having high accuracy as well as less voids. The reduction of voids by fusion, ranges from 40 to 85 % in different combinations of data.

Time Series Models

2013 ◽  
Author(s):  
James B. Elsner ◽  
Thomas H. Jagger
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Time Series Models ◽  
Infinite Length ◽  
Network Graph ◽  
Series Length ◽  
Single Time ◽  
Ergodic Hypothesis ◽  
Definition Of

In this chapter, we consider time series models. A time series is an ordered sequence of numbers with respect to time. In climatology, you encounter time-series data in a format given by . . . {h}Tt=1 = {h1,h2,. . . ,hT} (10.1) . . . where the time t is over a given season, month, week, or day and T is the time series length. The aim is to understand the underlying physical processes that produced the series. A trend is an example. Often by simply looking at a time series plot, you can pick out a trend that tells you that the process generating the data is changing. A single time series gives you a sample from the process. Yet under the ergodic hypothesis, a single time series of infinite length contains the same information (loosely speaking) as the collection of all possible series of finite length. In this case, you can use your series to learn about the nature of the process. This is analogous to spatial interpolation encountered in Chapter 9, where the variogram was computed under the assumption that the rainfall field is stationary. Here we consider a selection of techniques and models for time series data. We begin by showing you how to overlay plots as a tool for exploratory analysis. This is done to compare the variation between two series qualitatively. We demonstrate large variation in hurricane counts arising from a constant rate process. We then show techniques for smoothing. We continue with a change-point model and techniques for decomposing a continuous-valued series. We conclude with a unique way to create a network graph from a time series of counts and suggest a new definition of a climate anomaly. A plot showing your variables on a common time axis is an informative exploratory graph. Values from two different series are scaled to have the same relative range so the covariation in the variables can be compared visually. Here you do this with hurricane counts and sea-surface temperature (SST). Begin by loading annual.RData. These data were assembled in Chapter 6.

scholarly journals GRANGER CAUSALITY AND STRUCTURAL CAUSALITY IN CROSS-SECTION AND PANEL DATA

2016 ◽  
Vol 33 (2) ◽  
pp. 263-291 ◽  
Author(s):  
Xun Lu ◽  
Liangjun Su ◽  
Halbert White
Keyword(s):  
Time Series ◽  
Panel Data ◽  
Cross Section ◽  
Conditional Independence ◽  
Time Series Data ◽  
Structural Heterogeneity ◽  
Series Data ◽  
Direct Link ◽  
Structural Effects ◽  
Definition Of

Granger noncausality in distribution is fundamentally a probabilistic conditional independence notion that can be applied not only to time series data but also to cross-section and panel data. In this paper, we provide a natural definition of structural causality in cross-section and panel data and forge a direct link between Granger (G–) causality and structural causality under a key conditional exogeneity assumption. To put it simply, when structural effects are well defined and identifiable,G–non-causality follows from structural noncausality, and with suitable conditions (e.g., separability or monotonicity), structural causality also impliesG–causality. This justifies using tests ofG–non-causality to test for structural noncausality under the key conditional exogeneity assumption for both cross-section and panel data. We pay special attention to heterogeneous populations, allowing both structural heterogeneity and distributional heterogeneity. Most of our results are obtained for the general case, without assuming linearity, monotonicity in observables or unobservables, or separability between observed and unobserved variables in the structural relations.

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