| contributor author | Elsner, J. B. | |
| contributor author | Tsonis, A. A. | |
| date accessioned | 2017-06-09T14:40:58Z | |
| date available | 2017-06-09T14:40:58Z | |
| date copyright | 1992/01/01 | |
| date issued | 1992 | |
| identifier issn | 0003-0007 | |
| identifier other | ams-24383.pdf | |
| identifier uri | http://onlinelibrary.yabesh.ir/handle/yetl/4161049 | |
| description abstract | We present a brief overview of some new methodologies for making predictions on time-series data. These ideas stern from two rapidly growing fields: nonlinear dynamics (choas) theory and parallel distributed processing. Examples are presented that show the usefulness of such methods in making short-term predictions. It is suggested that such methodologies are capable of distinguishing between chaos and noise. Implications of these ideas and methods in the study of weather and climate are discussed. | |
| publisher | American Meteorological Society | |
| title | Nonlinear Prediction, Chaos, and Noise | |
| type | Journal Paper | |
| journal volume | 73 | |
| journal issue | 1 | |
| journal title | Bulletin of the American Meteorological Society | |
| identifier doi | 10.1175/1520-0477(1992)073<0049:NPCAN>2.0.CO;2 | |
| journal fristpage | 49 | |
| journal lastpage | 60 | |
| tree | Bulletin of the American Meteorological Society:;1992:;volume( 073 ):;issue: 001 | |
| contenttype | Fulltext | |
