Finding Articulated Body in Time-Series Volume Data

Search engine query data deliver insight into the behaviour of individuals who are the smallest possible scale of our economic life. Individuals are submitting several hundred million search engine queries around the world each day. We study weekly search volume data for various search terms from 2004 to 2010 that are offered by the search engine Google for scientific use, providing information about our economic life on an aggregated collective level. We ask the question whether there is a link between search volume data and financial market fluctuations on a weekly time scale. Both collective ‘swarm intelligence’ of Internet users and the group of financial market participants can be regarded as a complex system of many interacting subunits that react quickly to external changes. We find clear evidence that weekly transaction volumes of S&P 500 companies are correlated with weekly search volume of corresponding company names. Furthermore, we apply a recently introduced method for quantifying complex correlations in time series with which we find a clear tendency that search volume time series and transaction volume time series show recurring patterns.

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Finding Kinematic Structure in Time Series Volume Data

ELCVIA Electronic Letters on Computer Vision and Image Analysis ◽

10.5565/rev/elcvia.164 ◽

2009 ◽

Vol 7 (4) ◽

pp. 62 ◽

Cited By ~ 2

Author(s):

Tomoyuki Mukasa ◽

Shohei Nobuhara ◽

Atsuto Maki ◽

Takashi Matsuyama

Keyword(s):

Time Series ◽

Kinematic Structure ◽

Volume Data

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Lake Volume Data Analyses: A Deep Look into the Shrinking and Expansion Patterns of Lakes Azuei and Enriquillo, Hispaniola

Hydrology ◽

10.3390/hydrology7010001 ◽

2019 ◽

Vol 7 (1) ◽

pp. 1

Author(s):

Mahrokh Moknatian ◽

Michael Piasecki

Keyword(s):

Time Series ◽

Extreme Event ◽

Response Times ◽

Landsat Imagery ◽

Storm Events ◽

Volume Data ◽

Cyclone Activity ◽

Seasonal Periodicity ◽

Data Set ◽

Data Analyses

This paper presents the development of an evenly spaced volume time series for Lakes Azuei and Enriquillo both located on the Caribbean island of Hispaniola. The time series is derived from an unevenly spaced Landsat imagery data set which is then exposed to several imputation methods to construct the gap filled uniformly-spaced time series so it can be subjected to statistical analyses methods. The volume time series features both gradual and sudden changes the latter of which is attributed to North Atlantic cyclone activity. Relevant cyclone activity is defined as an event passing within 80 km and having regional monthly rainfall averages higher than a threshold value of 87 mm causing discontinuities in the lake responses. Discontinuities are accounted for in the imputation algorithm by dividing the time series into two sub-sections: Before/after the event. Using leave-p-out cross-validation and computing the NRMSE index the Stineman interpolation proves to be the best algorithm among 15 different imputation alternatives that were tested. The final time series features 16-day intervals which is subsequently resampled into one with monthly time steps. Data analyses of the monthly volume change time series show Lake Enriquillo’s seasonal periodicity in its behavior and also its sensitivity due to the occurrence of storm events. Response times feature a growth pattern lasting for one to two years after an extreme event, followed by a shrinking pattern lasting 5–7 years returning the lake to its original state. While both lakes show a remarkable long term increase in size starting in 2005, Lake Azuei is different in that it is much less sensitive to storm events and instead shows a stronger response to just changing seasonal rainfall patterns.

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A Variance Component Approach To Industry Cost Analysis

Journal of Agricultural and Applied Economics ◽

10.1017/s0081305200015004 ◽

1979 ◽

Vol 11 (2) ◽

pp. 35-40

Author(s):

Richard L. Kilmer ◽

Daniel S. Tilley

Keyword(s):

Time Series ◽

Degrees Of Freedom ◽

Time Series Data ◽

Series Data ◽

Pool Data ◽

Volume Data ◽

Cross Sectional ◽

Multiple Observations ◽

Long Run ◽

Short Run

Cost and volume data used in long-run cost studies often are observations from a single cross-section on firms or the average of multiple observations for each firm [1, 3, 5]. Averaging costs and volume over a time series is designed to eliminate the effect of short-run disturbances on the estimated long-run cost function. This practice results in a loss of information on the cost effects of short-run disturbances and significantly reduces the potential degrees of freedom that could result from pooling cross-sectional time-series data. In order to pool data, binary variables for each firm previously have been used to account for short-run fixed firm effects [4].

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The LDE-Type Flare Occurrence (1969-1993)

International Astronomical Union Colloquium ◽

10.1017/s0252921100025458 ◽

1994 ◽

Vol 144 ◽

pp. 279-282

Author(s):

A. Antalová

Keyword(s):

Time Series ◽

Fourier Analysis ◽

Sunspot Cycle ◽

List Type ◽

Type Number ◽

Solar Cycles ◽

Type Iv ◽

Long Duration ◽

Cycle Maximum ◽

Flare Index

AbstractThe occurrence of LDE-type flares in the last three cycles has been investigated. The Fourier analysis spectrum was calculated for the time series of the LDE-type flare occurrence during the 20-th, the 21-st and the rising part of the 22-nd cycle. LDE-type flares (Long Duration Events in SXR) are associated with the interplanetary protons (SEP and STIP as well), energized coronal archs and radio type IV emission. Generally, in all the cycles considered, LDE-type flares mainly originated during a 6-year interval of the respective cycle (2 years before and 4 years after the sunspot cycle maximum). The following significant periodicities were found:• in the 20-th cycle: 1.4, 2.1, 2.9, 4.0, 10.7 and 54.2 of month,• in the 21-st cycle: 1.2, 1.6, 2.8, 4.9, 7.8 and 44.5 of month,• in the 22-nd cycle, till March 1992: 1.4, 1.8, 2.4, 7.2, 8.7, 11.8 and 29.1 of month,• in all interval (1969-1992):a)the longer periodicities: 232.1, 121.1 (the dominant at 10.1 of year), 80.7, 61.9 and 25.6 of month,b)the shorter periodicities: 4.7, 5.0, 6.8, 7.9, 9.1, 15.8 and 20.4 of month.Fourier analysis of the LDE-type flare index (FI) yields significant peaks at 2.3 - 2.9 months and 4.2 - 4.9 months. These short periodicities correspond remarkably in the all three last solar cycles. The larger periodicities are different in respective cycles.

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