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Leaf area index (LAI) is a dimensionless quantity that characterizes plant canopies.It is defined as the one-sided green leaf area per unit ground surface area (LAI = leaf area / ground area, m 2 / m 2) in broadleaf canopies. [1]
In order to still use the Box–Jenkins approach, one could difference the series and then estimate models such as ARIMA, given that many commonly used time series (e.g. in economics) appear to be stationary in first differences. Forecasts from such a model will still reflect cycles and seasonality that are present in the data.
Time series datasets can also have fewer relationships between data entries in different tables and don't require indefinite storage of entries. [6] The unique properties of time series datasets mean that time series databases can provide significant improvements in storage space and performance over general purpose databases. [6]
The Pinaceae (/ p ɪ ˈ n eɪ s iː ˌ iː,-s i ˌ aɪ /), or pine family, are conifer trees or shrubs, including many of the well-known conifers of commercial importance such as cedars, firs, hemlocks, piñons, larches, pines and spruces. The family is included in the order Pinales, formerly known as Coniferales.
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.
Panel data is the general class, a multidimensional data set, whereas a time series data set is a one-dimensional panel (as is a cross-sectional dataset). A data set may exhibit characteristics of both panel data and time series data. One way to tell is to ask what makes one data record unique from the other records.
For example, time series are usually decomposed into: , the trend component at time t, which reflects the long-term progression of the series (secular variation). A trend exists when there is a persistent increasing or decreasing direction in the data. The trend component does not have to be linear. [1]
Forecast either to existing data (static forecast) or "ahead" (dynamic forecast, forward in time) with these ARMA terms. Apply the reverse filter operation (fractional integration to the same level d as in step 1) to the forecasted series, to return the forecast to the original problem units (e.g. turn the ersatz units back into Price).