# Conceptual understanding

Jun 29th, 2015
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A time series is a sequence of observations of a random variable. It is a set of ordered observations on a quantitative characteristic of a phenomenon at equally spaced time points. In statistics, signal processing, econometrics and mathematical finance, a time series is a sequence of data points, measured.

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Contents TOPICS Page no.Chapter 11. Conceptual understanding:1.1 Definition of Time Series: 1.1.1 Forecasting 1.1.1 Modelling 1.2 Types of Forecasting Time Series1.2.1 Autoregressive integrated moving average (ARIMA)Box-Jenkins 1.3 New terms:1.2.2 Stationary process1.2.3 ErgodicityChapter 22. Applications of forecasting andmodellingChapter 1 Introduction1.1 Definition of Time Series: A time series is a sequence of observations of a random variable. It is a set of ordered observations on a quantitative characteristic of a phenomenon at equally spaced time points. In statistics, signal processing, econometrics and mathematical finance, a time series is a sequence of data points, measured typically at successive times spaced at uniform time intervals. A time series is a set of numbers that measures the status of some activity over time. It is the historical record of some activity, with measurements taken at equally spaced intervals (exception: monthly) with a consistency in the activity and the method of measurement.Examples include the monthly demand for a product, the annual freshman enrollment in a department of a university, and the daily volume of flows in a river.An example of a time series for 25 periods is plotted in Fig. 1 from the numerical data in the Table 1. The data might represent the weekly demand for some product. We use x to indicate

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