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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.
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]
Cointegration is a statistical property of a collection (X 1, X 2, ..., X k) of time series variables. First, all of the series must be integrated of order d.Next, if a linear combination of this collection is integrated of order less than d, then the collection is said to be co-integrated.
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.
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]
X-13ARIMA-SEATS, successor to X-12-ARIMA and X-11, is a set of statistical methods for seasonal adjustment and other descriptive analysis of time series data that are implemented in the U.S. Census Bureau's software package. [3]
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.
Given a time series of data x t, the STAR model is a tool for understanding and, perhaps, predicting future values in this series, assuming that the behaviour of the series changes depending on the value of the transition variable. The transition might depend on the past values of the x series (similar to the SETAR models), or exogenous variables.