𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Building neural network models for time series: a statistical approach

✍ Scribed by Marcelo C. Medeiros; Timo Teräsvirta; Gianluigi Rech

Publisher
John Wiley and Sons
Year
2005
Tongue
English
Weight
360 KB
Volume
25
Category
Article
ISSN
0277-6693

No coin nor oath required. For personal study only.

✦ Synopsis

This paper is concerned with modelling time series by single hidden layer feedforward neural network models. A coherent modelling strategy based on statistical inference is presented. Variable selection is carried out using simple existing techniques. The problem of selecting the number of hidden units is solved by sequentially applying Lagrange multiplier type tests, with the aim of avoiding the estimation of unidentified models. Misspecification tests are derived for evaluating an estimated neural network model. All the tests are entirely based on auxiliary regressions and are easily implemented. A smallsample simulation experiment is carried out to show how the proposed modelling strategy works and how the misspecification tests behave in small samples. Two applications to real time series, one univariate and the other multivariate, are considered as well. Sets of one-step-ahead forecasts are constructed and forecast accuracy is compared with that of other nonlinear models applied to the same series.

📜 SIMILAR VOLUMES

Neural networks and volterra series for
Neural networks and volterra series for time-domain power amplifier behavioral models
✍ F. Giannini; P. Colantonio; G. Orengo; A. Serino; G. Stegmayer; M. Pirola; G. Gh 📂 Article 📅 2007 🏛 John Wiley and Sons 🌐 English ⚖ 450 KB

This paper presents a black-box model that can be applied to characterize the nonlinear dynamic behavior of power amplifiers (PAs), including strong nonlinearities and memory effects. Feedforward time-delay Neural Networks (TDNN) are used to extract the model from a large-signal input-output time-do

A systematic approach to the input deter
A systematic approach to the input determination for neural network rainfall–runoff models
✍ Gwo-Fong Lin; Guo-Rong Chen 📂 Article 📅 2008 🏛 John Wiley and Sons 🌐 English ⚖ 289 KB

## Abstract Input determination has a great influence on the performance of artificial neural network (ANN) rainfall–runoff models. To improve the performance of ANN models, a systematic approach to the input determination for ANN models is proposed. In the proposed approach, the irrelevant inputs

A dynamic linear model approach for disa
A dynamic linear model approach for disaggregating time series data
✍ M. Al-Osh 📂 Article 📅 1989 🏛 John Wiley and Sons 🌐 English ⚖ 703 KB

An approach is proposed for obtaining estimates of the basic (disaggregated) series, xi, when only an aggregate series, y r , of k period non-overlapping sums of xi's is available. The approach is based on casting the problem in a dynamic linear model form. Then estimates of xi can be obtained by ap

Neural networks and the multinomial logi
Neural networks and the multinomial logit for brand choice modelling: a hybrid approach
✍ Yves Bentz; Dwight Merunka 📂 Article 📅 2000 🏛 John Wiley and Sons 🌐 English ⚖ 260 KB 👁 2 views

The study of brand choice decisions with multiple alternatives has been successfully modelled for more than a decade using the Multinomial Logit model. Recently, neural network modelling has received increasing attention and has been applied to an array of marketing problems such as market response

Univariate modelling of monthly maximum
Univariate modelling of monthly maximum temperature time series over northeast India: neural network versus Yule–Walker equation based approach
✍ Surajit Chattopadhyay; Deepak Jhajharia; Goutami Chattopadhyay 📂 Article 📅 2011 🏛 John Wiley and Sons 🌐 English ⚖ 234 KB

## Abstract The present paper has adopted an autoregressive approach to inspect the time series of monthly maximum temperature (__T__~max~) over northeast India. Through autocorrelation analysis the __T__~max~ time series of northeast India is identified as non‐stationary, with a seasonality of 12