This is an early version of a set of notes for the course on Time Series Analysis offered at Lund University. Any and all comments and suggestions are most welcome and appreciated. Glossary and Abbreviations 1-D, 2-D, etc. One-dimensional, two-dimensional, etc. ACF Auto-covariance function AR Autoregressive ARIMA Integrated ARMA ARMA Autoregressive moving average ARMAX ARMA with exogenous input...

This work characterized mega plasmid pSY153-MDR, carrying blaIMP-45 and armA, from a multidrug-resistant (MDR) Pseudomonas putida isolate from the urine of a cerebral infarction patient in China. The backbone of pSY153-MDR was closely related to Pseudomonas plasmids p12969-DIM, pOZ176, pBM413, pTTS12, and pRBL16, and could not be assigned to any of the known incompatibility groups. The accessor...

In this work, a powerful parametric spectral estimation technique, 2D-auto regressive moving average modeling (ARMA), has been applied to contrast transfer function (CTF) detection in electron microscopy. Parametric techniques such as auto regressive (AR) and ARMA models allow a more exact determination of the CTF than traditional methods based only on the Fourier transform of the complete imag...

In recent work, ARMARKOV representations have been proposed as an extension of ARMA representations of nite-dimensional linear time-invariant systems. ARMARKOV representations have the same form as ARMA representations, but explicitly involve Markov parameters. This paper generalizes ARMA least-squares time-domain identi cation to ARMARKOV representations. The ARMARKOV/least-squares identi cati...

This note provides a simple sufficient condition ensuring that solutions of stochastic delay differential equations (SDDEs) driven by subordinators are nonnegative. While, to the best our knowledge, no nonnegativity conditions available in context SDDEs, we compare result literature within subclass invertible continuous-time ARMA (CARMA) processes. In particular, analyze why cannot be necessary...

Under 5 years . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 6.5 5 to 9 years . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 5.8 10 to 14 years . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 5.7 15 to 19 years . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 6.0 20 to 24 years . . . . . . . . . . . . . . . . . . . . . . ....

7. Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 8. Supplemental Appendix A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 8.1. Description of Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 8.2. Assumption V1 for Vector β . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 ...

Understanding the predictability of a streamflow process is important for making decision for flood control and water management. However not much attention has been paid to the predictability study of streamflow processes so far. Predictability may be broken down into two categories: (1) model predictability and (2) potential predictability. The widely used model performance measure, coefficie...