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In theory, classic RNNs can keep track of arbitrary long-term dependencies in the input sequences. The problem with classic RNNs is computational (or practical) in nature: when training a classic RNN using back-propagation, the long-term gradients which are back-propagated can "vanish", meaning they can tend to zero due to very small numbers creeping into the computations, causing the model to ...
A First Course on Time Series Analysis – an open source book on time series analysis with SAS (Chapter 7) Box–Jenkins models in the Engineering Statistics Handbook of NIST; Box–Jenkins modelling by Rob J Hyndman; The Box–Jenkins methodology for time series models by Theresa Hoang Diem Ngo
Neural networks are typically trained through empirical risk minimization.This method is based on the idea of optimizing the network's parameters to minimize the difference, or empirical risk, between the predicted output and the actual target values in a given dataset. [4]
The Bass model has been widely used in forecasting, especially new product sales forecasting and technology forecasting. Mathematically, the basic Bass diffusion is a Riccati equation with constant coefficients equivalent to Verhulst—Pearl logistic growth. In 1969, Frank Bass published his paper on a new product growth model for consumer ...
This is an accepted version of this page This is the latest accepted revision, reviewed on 24 January 2025. Family of Unix-like operating systems This article is about the family of operating systems. For the kernel, see Linux kernel. For other uses, see Linux (disambiguation). Operating system Linux Tux the penguin, the mascot of Linux Developer Community contributors, Linus Torvalds Written ...