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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.

Models of time series analysis include: Classification: Identifies and assigns categories to the data. Curve fitting: Plots the data along a curve to study the relationships of variables within the data. Descriptive analysis: Identifies patterns in time series data, like trends, cycles, or seasonal variation.

Time-series data is structured sequentially, with observations ordered chronologically based on their associated timestamps or time intervals. It explicitly incorporates the temporal aspect, allowing for the analysis of trends, seasonality, and other dependencies over time.

Model time is the virtual (simulated) time maintained by the AnyLogic simulation engine. The model time has nothing to do with the real time or the computer clock (although you can run the model in a scale to real time).

Models of time series analysis include: Classification: Identifies and assigns categories to the data. Curve fitting: Plots the data along a curve to study the relationships of variables within the data. Descriptive analysis: Identifies patterns in time series data, like trends, cycles, or seasonal variation.

Two of the most common models in time series are the Autoregressive (AR) models and the Moving Average (MA) models. The autoregressive model uses observations from preivous time steps as input to a regression equations to predict the value at the next step.

Time series refers to a sequence of observations following each other in time, where adjacent observations are correlated. This can be used to model, simulate, and forecast behavior for a system. Time series models are frequently used in fields such as economics, finance, biology, and engineering.