Forecasting Global Temperature Variations by Neural Networks.

Abstract

Global temperature variations between 1861 and 1984 are forecast using regularization networks, multilayer perceptrons and linear autoregression. The regularization network, optimized by stochastic gradient descent associated with colored noise, gives the best forecasts. For all the models, prediction errors noticeably increase after 1965. These results are consistent with the hypothesis that the climate dynamics is characterized by low-dimensional chaos and that the it may have changed at some point after 1965, which is also consistent with the recent idea of climate change. (MM)

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1994
Accession Number
ADA290081

Entities

People

  • Federico Girosi
  • Takaya Miyano

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Artificial Intelligence
  • Climate Change
  • Cognitive Science
  • Data Sets
  • Delphi Method
  • Dynamics
  • Information Systems
  • Neural Networks
  • Power Spectra
  • Semiconductor Devices
  • Semiconductors
  • Sine Waves
  • Standards
  • Test Sets
  • White Noise

Readers

  • Computational Modeling and Simulation
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks