Time series prediction with improved neuro-endocrine model
نویسندگان
چکیده
منابع مشابه
Spline-based neuro-fuzzy Kolmogorov's network for time series prediction
A spline-based modification of the previously developed Neuro-Fuzzy Kolmogorov's Network (NFKN) is proposed. In order to improve the approximation accuracy, cubic B-splines are substituted for triangular membership functions. The network is trained with a hybrid learning rule combining least squares estimation for the output layer and gradient descent for the hidden layer. The initialization of...
متن کاملImproved Time Series Prediction and Symbolic Regression with Affine Arithmetic
We show how affine arithmetic can be used to improve both the performance and the robustness of genetic programming for problems such as symbolic regression and time series prediction. Affine arithmetic is used to estimate conservative bounds on the output range of expressions during evolution, which allows us to discard trees with potentially infinite bounds, as well as those whose output rang...
متن کاملTime Series Model for Bankruptcy Prediction via Adaptive Neuro- Fuzzy Inference System
Bankruptcy prediction has been addressed by many researchers in the field of finance since few decades. One of the best approaches to deal with this issue is considering it as a classification problem. In this paper a time series prediction model of bankruptcy via Adaptive neuro-fuzzy inference system (ANFIS) is formulated, which is capable of predicting the bankruptcy of a firm for any future ...
متن کاملSemiparametric Bootstrap Prediction Intervals in time Series
One of the main goals of studying the time series is estimation of prediction interval based on an observed sample path of the process. In recent years, different semiparametric bootstrap methods have been proposed to find the prediction intervals without any assumption of error distribution. In semiparametric bootstrap methods, a linear process is approximated by an autoregressive process. The...
متن کاملTime Prediction Using a Neuro-Fuzzy Model for Projects in the Construction Industry
This paper presents a prediction model based on a new neuro-fuzzy algorithm for estimating time in construction projects. The output of the proposed prediction model, which is employed based on a locally linear neuro-fuzzy (LLNF) model, is useful for assessing a project status at different time horizons. Being trained by a locally linear model tree (LOLIMOT) learning algorithm, the model is int...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Neural Computing and Applications
سال: 2013
ISSN: 0941-0643,1433-3058
DOI: 10.1007/s00521-013-1373-3