"In mathematics, a time series is a series of data points indexed (or listed or graphed) in time order."
A sequence of observations over time.
Introduction to Time Series Analysis: This topic covers the basic concepts and principles of time series analysis, its applications, and its importance in various fields.
Time Series Components: This topic covers the different components of a time series, such as trend, seasonality, and noise.
Stationarity: This topic covers the concept of stationarity in time series analysis, including its types and how to test for it.
Autocorrelation and Partial Autocorrelation: This topic covers the methods for measuring and interpreting the correlation between observations in a time series.
White Noise: This topic covers the concept of white noise and its importance in time series analysis.
Moving Average Models: This topic covers the use of moving average models in forecasting time series data.
Autoregressive Models: This topic covers the use of autoregressive models in forecasting time series data.
ARMA Models: This topic covers the combined use of autoregressive and moving average models in forecasting time series data.
ARIMA Models: This topic covers the use of integrated autoregressive and moving average models in forecasting time series data.
Seasonal ARIMA Models: This topic covers the use of seasonal ARIMA models in forecasting time series data.
Exponential Smoothing Models: This topic covers the use of exponential smoothing models in forecasting time series data.
Fourier Analysis: This topic covers the use of Fourier analysis in time series analysis, including its applications in identifying periodic patterns in data.
Wavelet Analysis: This topic covers the use of wavelet analysis in time series analysis, including its applications in identifying short-term and long-term patterns in data.
Time series clustering: This topic covers the application of clustering techniques to time series data, including supervised and unsupervised techniques.
Time Series Classification: This topic covers the application of classification techniques to time series data, including deep learning models.
Granger Causality: This topic covers the use of Granger causality in time series analysis, including its applications in determining causality between variables.
Multivariate Time Series Analysis: This topic covers the analysis of time series data with multiple variables, including techniques for modeling and forecasting.
Bayesian Time Series Analysis: This topic covers the use of Bayesian techniques in time series analysis, including Bayesian hierarchical models.
State Space Models: This topic covers the use of state space models in time series analysis, including Kalman filter and particle filter techniques.
Non-Linear Time Series Analysis: This topic covers the analysis of non-linear time series data, including chaos theory and nonlinear forecasting techniques.
Trend: A gradual, long-term increase or decrease in the data over time.
Seasonality: Regular, recurring patterns that occur within a fixed time period, such as daily, weekly, monthly or yearly.
Cycle: Non-seasonal patterns that occur with a period longer than one year.
Irregular: Random fluctuations that cannot be attributed to any identified pattern, and which can occur at any time.
Autoregressive: A time series model where past observations are used to predict future values.
Moving-average: A time series model where a moving window of past observations is used to predict future values.
ARMA: An autoregressive moving-average model where both past observations and a moving window are used to predict future values.
ARIMA: An autoregressive integrated moving-average model that includes a differencing term to remove trend and seasonality in the data.
SARIMA: A seasonal ARIMA model that includes seasonality in the differencing term.
Exponential smoothing: A time series model that assigns greater weight to recent observations, and lesser weight to older observations, with the aim of predicting future values.
State-space: A time series model where the underlying process is assumed to be a hidden variable that can be inferred from the observed data.
Vector autoregression (VAR): A multivariate time series model that accounts for the interdependence between multiple time series.
Structural Time Series: A time series model that decomposes the time series into multiple components including trend, seasonality and irregularities to model and forecast the series.
"Most commonly, a time series is a sequence taken at successive equally spaced points in time."
"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'."
"Time series analysis is distinct from cross-sectional studies, in which there is no natural ordering of the observations."
"Time series analysis is also distinct from spatial data analysis where the observations typically relate to geographical locations."
"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."
"Values for a given period will be expressed as deriving in some way from past values, rather than from future values."
"Time series analysis can be applied to real-valued, continuous data."
"Time series analysis can be applied to discrete numeric data."
"Time series analysis can be applied to discrete symbolic data."
"Sequences of characters, such as letters and words in the English language."
"A series of data points indexed (or listed or graphed) in time order."
"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."
"The use of a model to predict future values based on previously observed values."
"Time series models will often make use of the natural one-way ordering of time."