✦ LIBER ✦

A heuristic method for estimating time-series models for forecasting. I

✍ Scribed by Mel Appelbaum; Chris P. Tsokos

Publisher: Elsevier Science
Year: 1985
Tongue: English
Weight: 530 KB
Volume: 16
Category: Article
ISSN: 0096-3003
DOI: 10.1016/0096-3003(85)90010-4

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

A forecasting procedure for nonlinear au

A forecasting procedure for nonlinear autoregressive time series models

✍ Yuzhi Cai 📂 Article 📅 2005 🏛 John Wiley and Sons 🌐 English ⚖ 156 KB 👁 1 views

## Abstract Forecasting for nonlinear time series is an important topic in time series analysis. Existing numerical algorithms for multi‐step‐ahead forecasting ignore accuracy checking, alternative Monte Carlo methods are also computationally very demanding and their accuracy is difficult to contro

ARARMA models for time series analysis a

ARARMA models for time series analysis and forecasting

✍ Emanuel Parzen 📂 Article 📅 1982 🏛 John Wiley and Sons 🌐 English ⚖ 765 KB

## Abstract Methods of time series forecasting are proposed which can be applied automatically. However, they are not rote formulae, since they are based on a flexible philosophy which can provide several models for consideration. In addition it provides diverse diagnostics for qualitatively and qu

Weighted fuzzy time series models for TA

Weighted fuzzy time series models for TAIEX forecasting

✍ Hui-Kuang Yu 📂 Article 📅 2005 🏛 Elsevier Science 🌐 English ⚖ 236 KB

A fractal forecasting model for financia

A fractal forecasting model for financial time series

✍ Gordon R. Richards 📂 Article 📅 2004 🏛 John Wiley and Sons 🌐 English ⚖ 149 KB 👁 1 views

## Abstract Financial market time series exhibit high degrees of non‐linear variability, and frequently have fractal properties. When the fractal dimension of a time series is non‐integer, this is associated with two features: (1) inhomogeneity—extreme fluctuations at irregular intervals, and (2) s

Order series method for forecasting non-

Order series method for forecasting non-Gaussian time series

✍ Ming-De Chuang; Gwo-Hsing Yu 📂 Article 📅 2007 🏛 John Wiley and Sons 🌐 English ⚖ 287 KB

## Abstract A new forecasting non‐Gaussian time series method based on order series transformation properties has been proposed. The proposed method improves Yu's method without using Hermite polynomial expansion to process nonlinear instantaneous transformations and provides acceptable forecasting

Hybrid neural network models for hydrolo

Hybrid neural network models for hydrologic time series forecasting

✍ Ashu Jain; Avadhnam Madhav Kumar 📂 Article 📅 2007 🏛 Elsevier Science 🌐 English ⚖ 163 KB