Time Distance – A New Generic Approach for Analysis and Visualiziation of Time Related Data

author: Pavle Sicherl, University of Ljubljana

Description

The art of handling different views of data is crucial for discovering the relevant patterns and for providing a broader framework for policy analysis. The new generic time distance approach (with associated novel statistical measure S-time-distance) offers a new view of data that is exceptionally easy to understand and communicate, and it allows for developing and exploring new hypotheses and perspectives.

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*Slides*
0:00	SICENTER Ljubljana, Slovenia Time Distance – New Generic Approach for Analysis and Visulatisation of Time Related Data Professor Pavle Sicherl
0:30	TIME DISTANCE: Concept and novel generic statistical measure
1:41	A Comparison of European and US Economies Based on Time Distances
4:24	A NEW VIEW IN TIME SERIES ANALYSIS
6:09	A new view of the information using levels of the variable as identifiers and time as the focus of comparison and numeraire
6:40	The resulting time matrix provides new information from which new generic measures can be derived. Two operators applied to this time matrix lead to the derivation of two novel statistical measures,
8:01	Source: P. Sicherl, Time Distance: A Missing Link in Comparative Analysis, 28th General Conference of the International Association for Research in Income and Wealth, Cork, Ireland, August 22-28 2004
8:26	An example how analytical statistical tables can present time dimension in a new way Time matrix: time when a given indicator level was attained (penetration rates for PC per 100 inh.)
9:26	TITLE
10:18	METHODOLOGY: a broader perception, policy and welfare
10:57	The perception whether the gender difference in life expectancy in the EU15 is large or small depends on the measure used: static percentage difference is only 8 percent, while S-time-distance amounts
11:39	Importance for development strategy: the relations between growth, efficiency and inequality are different when based on a dynamic concept of overall degree of disparity
13:12	UNITS OF COMPARISON
13:52	FIELDS OF CONCERN
14:15	TYPES OF ANALYSIS
14:50	Comparisons over many indicators can show characteristic profiles across countries, regions, socio-economic groups, firms, etc.
16:01	The time lag behind the USA over time has been the highest for employment rate and R&D per capita (25 and 23 years)
16:33	Time distance measure is intuitively understood by policy makers, managers, media and general public and is comparable across different variables, fields of concern, and units of comparison.
17:51	S-time-distances for buying online for sectors compared with the average percentage of diffusion for all sectors
18:34	Growth rate effects and a broader concept of convergence
19:44	The generic idea for many other applications of S-time-distance
21:13	Monitoring and goodness-of-fit test in two dimensions
22:34	TITLE
22:35	TITLE
23:13	TITLE
23:42	Unemployment rate as a function of capacity utilization rate Deviations in indicator and time, USA 1979-88
23:53	Frequency distribution of a daily variable for two dimensions: level and time
24:35	Source: P.Sicherl, Time Distance – Another Measure of Achieving Lisbon Targets, European Regional Economic Forum EREF-2005 Nova Gorica, Slovenia, 27-28 October 2005
25:04	Source: P.Sicherl, ibid.
25:25	BENEFITS OF USING TIME DISTANCE ANALYSIS
26:32	TITLE
27:29	SUMMARY: Benefits of immediate operational uses of time distance

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