The forecasting process of real-world time series has to deal with especially unexpected values, commonly known as outliers. Outliers in time series can lead to unreliable modeling and poor forecasts. Therefore, the identification of future outlier occurrence is an essential task in time series analysis to reduce the average forecasting error. The main goal of this work is to predict the occurr...

In this paper†, we consider the problem of indoor surveillance and propose a feature selection scheme for occupancy classification in an indoor environment. The classifier aims to determine whether there is exactly one occupant or more than one occupant. Data are obtained from six seismic sensors (geophones) that are deployed in a typical building hallway. Four proposed features exploit amplitu...

The Mann-Kendall test is a commonly-used nonparametric test for time trend. However, the standard P-values obtained from it are based on an assumption of independence between observations (since the theory is that of the Kendall correlation). However, observations in time series are often autocorrelated: knowing that one observation is larger than the mean may tell you that the next observation...

Machines of all kinds from vehicles to industrial equipment are increasingly instrumented with hundreds of sensors. Using such data to detect “anomalous” behaviour is critical for safety and efficient maintenance. However, anomalies occur rarely and with great variety in such systems, so there is often insufficient anomalous data to build reliable detectors. A standard approach to mitigate this...

Zooplankton are considered good indicators for understanding how oceans are affected by climate change. While climate influence on zooplankton abundance variability is currently accepted, its mechanisms are not understood, and prediction is not yet possible. This paper utilizes the Genetic Programming approach to identify which environmental variables, and at which extent, can be used to expres...

An insu ciency of training data often results in a poorly learned classi er. To mitigate this problem, several learning methods using both labeled and unlabeled data have been proposed. In these methods, however, only static data are considered; time series unlabeled data cannot be utilized. In this paper, we rst present an extension of HMMs, named Extended Tied-Mixture HMMs (ETM-HMMs) in which...

This study examined the spatial and temporal rainfall variability from the 1940s to 2007 in the south west coastal region of Bangladesh. Time series statistical tests were applied to examine the spatial and temporal trends in three time segments (1948–1970, 1971–1990 and 1991–2007) and four seasons (Pre-monsoon; Monsoon; Post-Monsoon and Winter), during the period 1948–2007. Eight weather stati...

Introduction and Motivation: fMRI is a natural source of high-dimensional time series data. Recordings are typically acquired in several hour sessions, with processing and analysis done offline. Due to the short amount of recording time available from any session, there is an imbalance between the comparatively small number of time slices and the high dimensionality of the data at each slice. A...

The Himalayan mountain forest ecosystem has been degrading since the British ruled the area in the 1850s. Local understanding of the patterns and processes of degradation is desperately required to devise management strategies to halt this degradation and provide long-term sustainability. This work comprises a satellite image based study in combination with national expert validation to generat...

background : the ifa test is one of the most usual methods for detecting anti- toxoplasma antibod­ies, although it has not any unique standardization. it seems that the microscopic judg­ment of results is an important confounder in ifa test. therefore, we conducted the present study to clarify the role of microscopic observer, and other confounders on the test. methods: eighty sera were collect...