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نتیجه جستجو - الگوی فصلی

تعداد مقالات یافته شده: 2
ردیف عنوان نوع
1 Measuring tourism seasonality across European countries
سنجش فصلی بودن گردشگری دربین کشورهای اروپایی-2018
This paper will propose a general approach for the analysis and measurement of seasonality in tourism, based on an analysis of the pattern of seasonal swing, as a preliminary step for the assessment of seasonal amplitude. The seasonality of tourism demand across European countries will be analyzed and clusters of countries identified, which are based on a similarity of their seasonal pattern. After discussing the limitations of the most frequently used indices employed in the tourism literature, a new index for measuring seasonality in tourism will be suggested in order to measure seasonal amplitude. The latter takes into account the ordinal and cyclical structures of seasonal variations. The results demonstrate a strong connection between seasonal patterns and the spatial distribution throughout European countries, which may orient future policy actions for dealing with seasonality on a European level.
keywords: Seasonality index |Gini index |Seasonal variations |Seasonal pattern |Seasonal amplitude |Europe
مقاله انگلیسی
2 G-SPAMINE: An approach to discover temporal association patterns and trends in internet of things
G-SPAMINE: یک رویکرد برای کشف الگوهای انجمنی موقت و روندها در اینترنت اشیاء -2017
Temporal data is one of the most common form of data in internet of things. Data from various sources such as sensors, smart phones, smart homes and smart vehicles in near future shall be of temporal nature with generated information recorded at different timestamps. We call all such data as time stamped temporal data. Discovery of temporal patterns and temporal trends from such temporal data requires new algorithms and methodologies as most of the existing algorithms do not reveal emerging, seasonal and diminishing patterns. In this paper, the objective is to find temporal patterns whose true prevalence values vary similar to a reference support time sequence satisfying subset constraints through estimating temporal pattern support bounds and using a novel fuzzy dissimilarity measure. We name our approach as G-SPAMINE. Experiment results show that G-SPAMINE out performs naive and sequential approaches and comparatively better to or atleast same as SPAMINE. In addition, the stamped temporal data adds extra level of privacy for temporal patterns in the IoT.
Keywords: Temporal data | Seasonal pattern | Support bounds | Temporal trend | Web of things
مقاله انگلیسی
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