Nonlinear Strategy to Classify Time Series of the Semiconductor Market Trend
| Authors: | Bucolo Maide, Universita Degli Studi di Catania, Italy
Fortuna Luigi, Universita Degli Studi di Catania, Italy
Galvagno Luca, Universita Degli Studi di Catania, Italy
Caizzone Francesco, STMicroelectronics Catania site, Italy
Tomarchio Giuseppina, Universita Degli Studi di Catania, Italy |
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| Topic: | 9.1 Economic & Business Systems |
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| Session: | Economic Systems |
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| Keywords: | Time series, Decision Supporting System, Products, Neural-Networks, Classification. |
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Abstract
The task of solving the management of production planning with a wide products portfolio is not an easy one. The paper deals with the adoption of an unsupervised clustering strategy to classify products, not just in relation with the business parameters, but also by considering the historical evolution of sales and customer demand. Different nonlinear techniques have been considered to face the problem both from the mathematical point of view than from the economic one. Particularly, this strategy has been implemented to classify the products of Discrete and Standard Ics Group of the worldwide semiconductor firm, STMicroelectronics in relation to their sales versus the different customer type.Two different approaches have been used: the hierarchical clustering with an optimization procedure and GHSOM structures. The results show the validity of both the cluster strategies.
