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In many cases, the repositories of time-series data will utilize compression algorithms to manage the data efficiently. [ 3 ] [ 4 ] Although it is possible to store time-series data in many different database types, the design of these systems with time as a key index is distinctly different from relational databases which reduce discrete ...
Cupressaceae is a widely distributed conifer family, with a near-global range in all continents except for Antarctica, stretching from 70°N in arctic Norway (Juniperus communis) [3] to 55°S in southernmost Chile (Pilgerodendron uviferum), further south than any other conifer species. [4] Juniperus indica reaches 4930 m altitude in Tibet. [5]
Most conifers are monoecious, but some are subdioecious or dioecious; all are wind-pollinated. Conifer seeds develop inside a protective cone called a strobilus. The cones take from four months to three years to reach maturity, and vary in size from 2 to 600 millimetres (1 ⁄ 8 to 23 + 5 ⁄ 8 in) long.
Encyclopedia of Conifers. A Comprehensive Guide to Conifer Cultivars and Species is an encyclopedia written by Aris G. Auders and Derek P. Spicer, published in 2012. The two-volume, extensively illustrated encyclopedia is a complete reference book covering all recognised conifer cultivars and species, both hardy and tropical.
Podocarpus (/ ˌ p oʊ d ə ˈ k ɑːr p ə s / [2]) is a genus of conifers, the most numerous and widely distributed of the podocarp family, the Podocarpaceae. Podocarpus species are evergreen shrubs or trees, usually from 1 to 25 m (3 to 82 ft) tall, known to reach 40 m (130 ft) at times.
The great majority of conifer genera and species are evergreen, retaining their leaves for several (2–40) years before falling, but unusual deciduous conifers occur in five genera (Larix, Pseudolarix, Glyptostrobus, Metasequoia and Taxodium), shedding their leaves in autumn and leafless through the winter.
SSA can be used as a model-free technique so that it can be applied to arbitrary time series including non-stationary time series. The basic aim of SSA is to decompose the time series into the sum of interpretable components such as trend, periodic components and noise with no a-priori assumptions about the parametric form of these components.
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