Forecasting complexity is a measure of complexity put forward (under the original name of) by the physicist Peter Grassberger.[1][2][3]
It was later renamed "statistical complexity" by James P. Crutchfield and Karl Young.[4][5]
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Grassberger, P. (1986). "Toward a quantitative theory of self-generated complexity". International Journal of Theoretical Physics. 25 (9): 907–938. Bibcode:1986IJTP...25..907G. doi:10.1007/bf00668821. S2CID 16952432.
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Grassberger, P. (2012). "Randomness, Information, and Complexity". arXiv:1208.3459 [physics].
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Funes, P. "Complexity measures for complex systems and complex objects". Retrieved 2012-08-04.
Forecastingcomplexity is a measure of complexity put forward (under the original name of) by the physicist Peter Grassberger. It was later renamed "statistical...
information content of an individual sequence), the statistical complexity, like forecastingcomplexity, implies a statistical description, and refers to an ensemble...
theory and applications Forecastingcomplexity Effective complexity Kolmogorov complexity, a measure of algorithmic complexity Self-dissimilarity in information...
(2006). "25 Tears of Time Series Forecasting". International Journal of Forecasting. Twenty Five Years of Forecasting. 22 (3): 443–473. CiteSeerX 10.1...
determining the predictive power of prognostic model forecasts. Because of the complexity of these models, forecast verification goes a good deal beyond simple...
anticipated payments and receivables. Several forecasting methodologies are available. Cash flow forecasting is an element of financial management. Maintaining...
Fits”. Technological Forecasting & Social Change. 46 (2): 153-173. Modis, Theodore (2020). “Forecasting the Growth of Complexity and Change—An Update”...
model ensemble forecasts have been used to help define the forecast uncertainty and to extend the window in which numerical weather forecasting is viable farther...
used for long-term forecasting. It was proved, that non-physical models of GMDH are more accurate for approximation and forecast than physical models...
Electricity price forecasting (EPF) is a branch of energy forecasting which focuses on using mathematical, statistical and machine learning models to...
Complex network Epidemic models on lattices Mutual information Forecastingcomplexity Kolmogorov entropy P. Grassberger; I. Procaccia (1983). "Measuring...
systematic, interactive forecasting method that relies on a panel of experts. Delphi has been widely used for business forecasting and has certain advantages...
Information fluctuation complexity is an information-theoretic quantity defined as the fluctuation of information about entropy. It is derivable from fluctuations...
Technological forecasting in perspective, OECD, 1967. 1968: Integrating Forecasting and Planning through a Function-Oriented Approach. Technological Forecasting for...
project, these are referred to as the four P's: Plan: The planning and forecasting activities. Process: The overall approach to all activities and project...
planner must use telecommunications forecasting methods to estimate the expected traffic intensity. The forecasting process involves several steps: Definition...
new science of probability. Flechtheim argued that even if systematic forecasting did no more than unveil the subset of statistically highly probable processes...
Applications of machine learning in earth sciences include geological mapping, gas leakage detection and geological features identification. Machine learning...
approach to forecasting, they can be divided into three main groups of activities (which are, generally speaking, common to all long range forecasting processes):...
well as the forecasting of precipitation amounts and distribution of the future. A variation of this theme, used for medium range forecasting, is known...
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