๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Fuzzy seasonality forecasting

โœ Scribed by Ping-Teng Chang

Publisher
Elsevier Science
Year
1997
Tongue
English
Weight
716 KB
Volume
90
Category
Article
ISSN
0165-0114

No coin nor oath required. For personal study only.

โœฆ Synopsis

In this paper, a fuzzy forecasting technique for seasonality in the time-series data is presented using the following procedure. First, with the fuzzy regression analysis the fuzzy trend of a time-series is analyzed. Then thefizzy season&y is defined by realizing the membership grades of the seasons to the fuzzy regression model. Both making fuzzy forecast and crisp forecast are investigated. Seasonal fuzziness and trends are analyzed. The method is applied to the sales forecasting problem of a food distribution company. 0 1997 Elsevier Science B.V.

๐Ÿ“œ SIMILAR VOLUMES

Application of fuzzy logic to forecast s
Application of fuzzy logic to forecast seasonal runoff
โœ C. Mahabir; F. E. Hicks; A. Robinson Fayek ๐Ÿ“‚ Article ๐Ÿ“… 2003 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 204 KB

## Abstract Each spring in Alberta, Canada, the potential snowmelt runoff is forecast for several basins to assess the water supply situation. Water managers need this forecast to plan water allocations for the following summer season. The Lodge Creek and Middle Creek basins, located in southeaster

Seasonal climate forecasting
Seasonal climate forecasting
โœ Alberto Troccoli ๐Ÿ“‚ Article ๐Ÿ“… 2010 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 604 KB

The fascination of seasonal climate forecasting, of which El Niรฑo forecasting is the prime example, comes from its multi-faceted character. Not only does it pose interesting new challenges for the climate scientific community but also it is naturally linked to a great variety of socio-economic appli

Forecasting methods using fuzzy concepts
Forecasting methods using fuzzy concepts
โœ Toly Chen; Mao-Jiun J. Wang ๐Ÿ“‚ Article ๐Ÿ“… 1999 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 649 KB

In this paper, fuzzy concepts are applied in forecasting product price and sales in the semiconductor industry which is often conceived as a highly dynamic environment. First, two fuzzy forecasting methods including fuzzy interpolation (FI) and fuzzy linear regression (FLR) are developed and discuss