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Applied Time Series Analysis

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Applied Time Series Analysis and Innovative Computing

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 59))

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Abstract

There are many reasons to analyze the time series data, for example, to understand the underlying generating mechanism better, to achieve optimal control of the system, or to obtain better forecasting of future values. Applied time series analysis consists of empirical models for analyzing time series in order to extract meaningful statistics and other properties of the time series data. Time series models have various forms and represent different stochastic processes. Time series analysis model is usually classified as either time domain model or frequency domain model. Time domain models include the auto-correlation and cross-correlation analysis. In a time domain model, mathematical functions are usually used to study the data with respect to time. The three broad classes for modeling the variations of time series process are the autoregressive models, the integrated models, and the moving average models. The autoregressive integrated moving average models are the general class of these models for forecasting a time series that can be stationarized by transformations such as differencing. In a frequency domain model, the analysis of mathematical functions or signals is conducted with respect to frequency rather than time. Mathematical models can be used to convert the time series data between the time and frequency domains. The parameters and features in the frequency domain can be used as inputs for the mathematical models like discrimination analysis and improved results can be obtained.

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Authors and Affiliations

  1. International Association of Engineers, Unit 1, 1/F, 37–39 Hung To Road, Hong Kong, China

    Sio-Iong Ao

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  1. Sio-Iong Ao
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Ao, SI. (2010). Applied Time Series Analysis. In: Applied Time Series Analysis and Innovative Computing. Lecture Notes in Electrical Engineering, vol 59. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-8768-3_2

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