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Time Series Analysis covers techniques for modeling, estimating, filtering and forecasting a stochastic process defined in discrete time (time series). Also, time series analysis covers techniques for computing various characteristics of a discrete time process, e.g. moments, finite-dimensional distributions, spectrum, etc. Most problems involving stochastic processes in finance, engineering, physics, earth sciences and biology can be rephrased as time series problems, as we can always choose high discretization to describe a phenomenon that seems to be continuously changing. Nonetheless, time series analysis is the way to go in less than 100% of the cases, because some of the derivations and proofs are more easily done in continuous time. Others are much nicer in discrete time, which has led to time series analysis being one of the biggest parts of econometrics.

**TIME SERIES ANALYSIS SUBCATEGORIES**

- Akaike Information Criterion
- ARIMA
- ARMA
- Bayesian Information Criterion
- Cross-validation
- Martingale
- Model Selection
- Stationary Process
- Stochastic Volatility Modeling
- White Noise

Greene, W. H. (2011). Econometric Analysis (7th ed). Upper Saddle River, NJ: Prentice Hall.

Hamilton, J. D. (1994). Time Series Analysis. Princeton University Press.

Brockwell, P. J., & Davis, R. A. (1991). Time Series: Theory and Methods (2nd ed). New York: Springer.

Wei, W. W. S. (1990). Time Series Analysis: Univariate and Multivariate Methods. Redwood City, CA: Addison Wesley.

Tsay, R. S. (2005). Analysis of Financial Time Series. New Jersey: Wiley-Interscience.

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