# Time series

In mathematics, a time series is a series of data points indexed (or listed or graphed) in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data. Examples of time series are heights of ocean tides, counts of sunspots, and the daily closing value 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, and largely in any domain of applied science and engineering which involves temporal measurements. Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values. While regression analysis is often employed in such 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 refers in particular to relationships between different points in time within a single series. Interrupted time series analysis is used to detect changes in the evolution of a time series from before to after some intervention which may affect the underlying variable. Time series data have a natural temporal ordering. This makes time series analysis distinct from cross-sectional studies, in which there is no natural ordering of the observations (e.g. explaining people's wages by 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 house prices by the location as well as the intrinsic characteristics of the houses). A stochastic model for a time series will generally reflect the fact that observations close together in time will be more closely related than observations further apart. In addition, time series models will often make use of the natural one-way ordering of time so that values for a given 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 real-valued, continuous data, discrete numeric data, or discrete symbolic data (i.e. sequences of characters, such as letters and words in the English language). (Wikipedia).

Why Are Time Series Special? : Time Series Talk

So ... what's so special about time series?

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Evaluating Time Series Models : Time Series Talk

How do we evaluate our time series models? How can we tell if one model is better than another?

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Time by clocks

The way how to show time using clocks. It is 12 hours video you can use as a screensaver on clock, every number changing is completely random. Please enjoy.

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Intro to stationarity in time series analysis My Patreon : https://www.patreon.com/user?u=49277905

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Time Series Talk : Lag Operator

Intro to the lag operator in time series analysis.

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Financial Time Series Processing

To learn more about Wolfram Technology Conference, please visit: https://www.wolfram.com/events/technologyconference/ Speaker: Anmol Bajracharya Wolfram developers and colleagues discussed the latest in innovative technologies for cloud computing, interactive deployment, mobile devices,

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For the latest information, please visit: http://www.wolfram.com Speaker: Gosia Konwerska Wolfram developers and colleagues discussed the latest in innovative technologies for cloud computing, interactive deployment, mobile devices, and more.

From playlist Wolfram Technology Conference 2016

Time Series Processing

For the latest information, please visit: http://www.wolfram.com Speaker: Gosia Konwerska Wolfram developers and colleagues discussed the latest in innovative technologies for cloud computing, interactive deployment, mobile devices, and more.

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Ehanced Probability & Statistics

Speaker: Gosia Konwerska and Devendra Kapadia Mathematica 10 includes major enhancements for probability and statistics. This presentation covers new functionality such as time series modeling, hidden Markov models, and transformations of random processes. It also explores functionality f

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QRM 6-1: TS for RM 1 (intro)

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Calculus 11.10 Taylor and Maclaurin Series

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