Open AccessBook
The Analysis of Time Series: An Introduction
Reads0
Chats0
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
Citations
More filters
Journal ArticleDOI
Predicting Subcutaneous Glucose Concentration in Humans: Data-Driven Glucose Modeling
TL;DR: Simulation results indicated that stable and accurate models for near-future glycemic predictions with clinically acceptable time lags are attained only when the raw glucose measurements are smoothed and the model coefficients are regularized.
Journal ArticleDOI
Effective fire suppression in boreal forests
TL;DR: This work analysed IA's (operational) effectiveness by a controlled retrospective study of fire-history data for an approximately 86 000 km2 region of boreal forest in northeastern Alberta, Canada, from 1968 to 1998, finding that over this interval, various improvements to IA practice created a natural experiment.
Journal ArticleDOI
A primer on two-level dynamic structural equation models for intensive longitudinal data in Mplus.
Daniel McNeish,Ellen L. Hamaker +1 more
TL;DR: The goal is to provide readers with a basic conceptual understanding of common models, template code, and result interpretation in time-series analysis so that the more advanced literature on the topic is more readily digestible to a larger group of researchers.
Journal ArticleDOI
Interdependence and predictability of human mobility and social interactions
TL;DR: It is shown that by means of multivariate nonlinear time series prediction techniques it is possible to increase the forecasting accuracy by considering movements of friends, people, or more in general entities, with correlated mobility patterns (i.e., characterised by high mutual information) as inputs.
Journal ArticleDOI
Detection of sub-8-nm movements of kinesin by high-resolution optical-trap microscopy
TL;DR: The distribution of magnitudes reveals that kinesin not only undergoes discrete 8-nm movements, in agreement with previous work, but also frequently exhibits smaller movements of about 5 nm, which is a possible explanation for these unexpected smaller movements.