Classification and prediction of hail using self-organizing neural networks

Ultsch, Alfred; Guimarães, Gabriela; Schmid, Willy A.

doi:10.1109/icnn.1996.549143

Cited by 6 publications

(4 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This type of learning has been used for standard time-series prediction (see e.g. [28,29]) and predictions through non-linear regression (see e.g. [10,11]).…”

Section: Related Workmentioning

confidence: 99%

Self-organizing time map: An abstraction of temporal multivariate patterns

Sarlin

2013

Neurocomputing

View full text Add to dashboard Cite

This paper adopts and adapts Kohonen's standard Self-Organizing Map (SOM) for exploratory temporal structure analysis. The Self-Organizing Time Map (SOTM) implements SOM-type learning to one-dimensional arrays for individual time units, preserves the orientation with short-term memory and arranges the arrays in an ascending order of time. The twodimensional representation of the SOTM attempts thus twofold topology preservation, where the horizontal direction preserves time topology and the vertical direction data topology. This enables discovering the occurrence and exploring the properties of temporal structural changes in data. For representing qualities and properties of SOTMs, we adapt measures and visualizations from the standard SOM paradigm, as well as introduce a measure of temporal structural changes. The functioning of the SOTM, and its visualizations and quality and property measures, are illustrated on artificial toy data. The usefulness of the SOTM in a real-world setting is shown on poverty, welfare and development indicators.

show abstract

“…This type of learning has been used for standard time-series prediction (see e.g. [28,29]) and predictions through non-linear regression (see e.g. [10,11]).…”

Section: Related Workmentioning

confidence: 99%

Self-organizing time map: An abstraction of temporal multivariate patterns

Sarlin

2013

Neurocomputing

View full text Add to dashboard Cite

show abstract

“…This often makes sense when an extrapolation of the data into the future is required, for instance, in time series prediction (Ultsch et al 1996), or in robot control (Walter, Schulten 1993).…”

Section: Self-organizing Maps For Time Series Analysis 149mentioning

confidence: 99%

“…In Ultsch et al (1996) a two step implementation of this approach was used for the prediction of hailstorm, first performing a classification of different types of hailstorm, in order to perform an enhanced prediction during a second prediction step.…”

Section: Self-organizing Maps For Time Series Analysis 149mentioning

confidence: 99%

“…The best results are usually obtained by carefully combining these methods. SOMs for temporal processing usually are used for one of the following purposes: prediction (Ultsch et al 1996;Koskela et al 1998), control (Ritter et al 1989;Ritter 1994), monitoring (Joutsiniemi et al 1995), and data mining (Guimaraes 2000;Guimaraes et al 2001a;Deboeck, Kohonen 1998).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Classification and Clustering in Business Cycle Analysis.

Heilemann¹,

Weihs²

2007

Schriften Des Rheinisch-Westfälischen Instituts Für Wirtschaftsforschung

View full text Add to dashboard Cite

PrefaceThe analysis of cyclical macroeconomic phenomena is an important field of econometric research. In the recent past, research interests have de-emphasized quantitative forecasting exercises and have addressed the qualitative diagnosis of the relative stance of the economy regarding "upswing", "recession", or "boom" periods, i. e. the classification of the state of the economy into a limited number of discrete states. In this context the principal challenge is to reduce the multifaceted and sometimes abundant quantitative information about the business cycle to such qualitative statements in an efficient way. For more than six years this task was the focus of the project "Multivariate determination and analysis of business cycles" within the SFB 475 "Reduction of complexity in multivariate data structures", funded by the German Research Foundation (DFG).The necessity for complexity reduction is, of course, not unique to business cycle analysis but is studied in many fields and in a number of ways. This broad interest in the reduction of problem dimensionality and in the appropriate combination of data and of theory caused the RWI Essen and the Statistical Department of the University of Dortmund in January 2002 to hold a workshop at the RWI Essen where the findings of this and similar projects were presented and discussed. The present publication collects revised versions of the papers presented at this workshop. Although the workshop took place some five years ago, these papers mark an importment juncture in the development of business cycle research.The RWI Essen thanks the editors of the volume and the authors for their contributions and the other participants for lively discussions. Particular thanks have to go to Joachim Schmidt who supervised the production of the volume and to Anette Hermanowski and Claudia Lohkamp whose support of the production of the manuscripts and of the organisation of the workshop was indispensable.Essen,

show abstract

Extending the SOM

Sarlin

2014

Computational Risk Management

View full text Add to dashboard Cite

Classification and prediction of hail using self-organizing neural networks

Cited by 6 publications

References 3 publications

Self-organizing time map: An abstraction of temporal multivariate patterns

Self-organizing time map: An abstraction of temporal multivariate patterns

Classification and Clustering in Business Cycle Analysis.

Extending the SOM

Contact Info

Product

Resources

About