shift changes and heterogeneity analyses of hydro-climatic variables is very important in water resources planning and management. in order to shift changes and heterogeneity analyses of tmin and tmax, precipitation and discharge, 2, 7 and 7 stations was used over the 40 years (1972-2011), respectively. the results of annual tmin and tmax series showed that both of two stations had the signific...

We present a methodology for learning complex dependencies in data based on streams of categorical, time series data. The streams representation is applicable in a variety of situations: a program's execution trace may be thought of as a stream. The various monitor readings of an intensive care unit may be thought of as concurrent streams. Our learning methodology, called dependency detection, ...

[1] A comparison of time series of basaltic and silicic eruptions in eastern California over the last 400 kyr with the contemporaneous global record of glaciation suggests that this volcanism is influenced by the growth and retreat of glaciers occurring over periods of about 40 kyr. Statistically significant cross correlations between changes in eruption frequency and the first derivative of th...

This paper develops an improved test of economic convergence or divergence using time series methods. The usefulness of the method is illustrated in an analysis of the growth pattern between Chinese regions in 1952-2007. Comparing all combinations of regional pairs, the analysis yields support for economic divergence in roughly half of the cases. In the other half, we instead find that regions ...

Many types of data collections processed by time series analysis often contain repeating similar episodes (patterns). If these patterns are recognized, then they may be used for instance in data compression, for prediction or for indexing large collections. Extraction of these patterns from data collections with components generated in equidistant time and in finite number of levels is now a tr...

While temporal behavioral patterns can be discerned to underlie real crowd work, prior studies have typically modeled worker performance under a simplified i.i.d. assumption. To better model such temporal worker behavior, we propose a time-series label prediction model for crowd work. This latent variable model captures and summarizes past worker behavior, enabling us to better predict the qual...

In this paper, we consider the temporal pattern in traffic flow time series, and implement a deep learning model for traffic flow prediction. Detrending based methods decompose original flow series into trend and residual series, in which trend describes the fixed temporal pattern in traffic flow and residual series is used for prediction. Inspired by the detrending method, we propose DeepTrend...

Wireless sensor networks (WSNs) represent a typical domain where there are complex temporal sequences of events. In this paper we propose a relational framework to model and analyse the data observed by sensor nodes of a wireless sensor network. In particular, we extend a general purpose relational sequence mining algorithm to tackle into account temporal interval-based relations. Real-valued t...