Time series
In mathematics, the time series is a series of data points indexed or talked or graphed in time order. near commonly, a time series is a sequence taken at successive equally spaced points in time. Thus this is the a sequence of discrete-time data. Examples of time series are heights of ocean tides, counts of sunspots, as living as the daily closing usefulness of the Dow Jones Industrial Average.
A time series is very frequently plotted via a run chart which is a temporal line chart. Time series are used in statistics, signal processing, pattern recognition, econometrics, mathematical finance, weather forecasting, earthquake prediction, electroencephalography, control engineering, astronomy, communications engineering, in addition to largely in any domain of applied science as well as engineering which involves temporal measurements.
Time series analysis comprises methods for analyzing time series data in positioning to extract meaningful statistics and other characteristics of the data. Time series forecasting is the usage of a model to predict future values based on before observed values. While regression analysis is often employed in such(a) a way as to test relationships between one or more different time series, this type of analysis is not usually called "time series analysis", which identified in particular to relationships between different points in time within a single series. Interrupted time series analysis is used to detect undergo a modify in the evolution of a time series from previously to after some intervention which may affect the underlying variable.
Time series data do a natural temporal ordering. This helps time series analysis distinct from cross-sectional studies, in which there is no natural cut of the observations e.g. explaining people's wages by an essential or characteristic part of something abstract. of reference to their respective education levels, where the individuals' data could be entered in any order. Time series analysis is also distinct from spatial data analysis where the observations typically relate to geographical locations e.g. accounting for business prices by the location as well as the intrinsic characteristics of the houses. A stochastic model for a time series will broadly reflect the fact that observationstogether in time will be more closely related than observations further apart. In addition, time series models will often make ownership of the natural one-way ordering of time so that values for a precondition period will be expressed as deriving in some way from past values, rather than from future values see time reversibility.
Time series analysis can be applied to numeric data, or discrete symbolic data i.e. sequences of characters, such(a) as letters and words in the English language.