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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.

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Time-sensitive Personalized Query Auto-Completion

TL;DR: A hybrid QAC model that considers both of these aspects: time-sensitivity and personalization is proposed that outperforms state-of-the-art time-sensitive QAC baseline and achieves total improvements of between 3% and 7% in terms of MRR.
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KIC 4552982: OUTBURSTS AND ASTEROSEISMOLOGY FROM THE LONGEST PSEUDO-CONTINUOUS LIGHT CURVE OF A ZZ Ceti

TL;DR: In this paper, the Kepler light curve of KIC 4552982, the first ZZ Ceti (hydrogen-atmosphere pulsating white dwarf star) discovered in the Kepler field of view, was presented.
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Funding begets biodiversity

TL;DR: Whether there is circularity between funding and perceived biodiversity, which may reinforce the conservation status of areas already perceived to be important while other areas with less initial funding may remain overlooked, and regional assessments of biodiversity conservation importance may be biased by investment.
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Analyzing growth components in trees

TL;DR: This work favors the hypothesis of an ontogenetic component structured as a succession of stationary phases and to highlight phase changes of high magnitude in unexpected situations (for instance, when growth globally decreases).
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A GA-weighted ANFIS model based on multiple stock market volatility causality for TAIEX forecasting

TL;DR: The proposed model uses the fluctuations of other national stock markets as forecasting factors and employs a genetic algorithm (GA) to refine the weights of rules joining in an ANFIS model to forecast the Taiwan stock index.