We propose a method to detect the onset of linear trend in a time series and estimate the change point T from the profile of a linear trend test statistic, computed on consecutive overlapping time windows along the time series. We compare our method to two standard methods for trend change detection and evaluate them with Monte Carlo simulations for different time series lengths, autocorrelatio...

The purpose of this paper is to propose a new likelihood-based panel cointegration test in the presence of a linear time trend in the data generating process. This new test is an extension of the likelihood ratio (LR) test of Saikkonen & Lütkepohl (2000) for trend-adjusted data to the panel data framework, and is called the panel SL test. The idea is first to take the average of the individual ...

Streaming applications from algorithmic trading to tra c management deploy Kleene patterns to detect and aggregate arbitrarily-long event sequences, called event trends. State-of-the-art systems process such queries in two steps. Namely, they first construct all trends and then aggregate them. Due to the exponential costs of trend construction, this two-step approach su↵ers from both a long del...

Change points in time series are perceived as isolated singularities where two regular trends of a given signal do not match. The detection of such transitions is of fundamental interest for the understanding of the system's internal dynamics or external forcings. In practice observational noise makes it difficult to detect such change points in time series. In this work we elaborate on a bayes...

Old agrochemical samples were analyzed for their dioxin impurities. Some herbicides were contaminated with 2,3,7,8-chlorine-substituted dioxin congeners. Using these new data as well as impurity and emission data reported in the literature, the time trends for dioxin emissions from the use of chlorinated aromatic compounds and some incineration activities have been estimated. The results indica...

We consider the use of generalized additive models with correlated errors for analysing trends in time series. The trend is represented as a smoothing spline so that it can be extrapolated. A method is proposed for choosing the smoothing parameter. It is based on the ability to predict a short term into the future. The choice not only addresses the purpose in hand, but also performs very well, ...

A jump process approach is proposed for the trend estimation of time series. The proposed jump process estimator can locally minimize two important features of a trend, the smoothness and 0delity, and explicitly balance the fundamental tradeo2 between them. A weighted average form of the jump process estimator is derived. The connection of the proposed approach to the Hanning 0lter, Gaussian ke...