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The Analysis of Time Series: An Introduction
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TLDR
In this paper, simple descriptive techniques for time series estimation in the time domain forecasting stationary processes in the frequency domain spectral analysis bivariate processes linear systems state-space models and the Kalman filter non-linear models multivariate time series modelling some other topics.Abstract:
Simple descriptive techniques probability models for time series estimation in the time domain forecasting stationary processes in the frequency domain spectral analysis bivariate processes linear systems state-space models and the Kalman filter non-linear models multivariate time series modelling some other topics.read more
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Journal ArticleDOI
Transfer function-noise modeling in continuous time using predefined impulse response functions
TL;DR: In this article, a method of transfer function-noise (TFN) modeling that operates in continuous time and uses a predefined family of impulse response (IR) functions is presented.
Journal ArticleDOI
An integrated fuzzy regression algorithm for energy consumption estimation with non-stationary data: A case study of Iran
TL;DR: An integrated fuzzy regression and time series framework to estimate and predict electricity demand for seasonal and monthly changes in electricity consumption especially in developing countries such as China and Iran with non-stationary data is presented.
Journal ArticleDOI
Extended orbitally forced palaeoclimatic records from the equatorial Atlantic Ceara Rise
TL;DR: In this article, the Oligocene-Miocene proxy records from Ocean Drilling Program (ODP) Leg 154 were used to evaluate how the interaction of long, multi-million year beats in the Earth's eccentricity and obliquity are implicated in the waxing and waning of ice-sheets, presumably on Antarctica.
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Predictive Monitoring for Improved Management of Glucose Levels
TL;DR: This study shows that, for a 30-minute prediction horizon, data-driven AR models provide sufficiently-accurate and clinically-acceptable estimates of glucose levels for timely, proactive therapy and should be considered as the modeling engine for predictive monitoring of patients with type 1 diabetes mellitus.
Proceedings ArticleDOI
Adaptive Normalization: A novel data normalization approach for non-stationary time series
Eduardo Ogasawara,Leonardo Conegundes Martinez,Daniel de Oliveira,Geraldo Zimbrão,Gisele L. Pappa,Marta Mattoso +5 more
TL;DR: Adaptive Normalization, a new method for normalizing non-stationary heteroscedastic (with non-uniform volatility) time series, was tested together with an Artificial Neural Network and showed AN improves ANN accuracy in both short- and long-term predictions.