Time-series analysis (TSA) is used in many fields, including finance, economics, meteorology and marketing.

Despite this, I occasionally hear the criticism that it doesn't actually work because these models assume normally distributed data and cannot handle non-linearities.

Academic researchers have studied time-series analysis for many decades, so this is not an obscure topic. A gigantic amount of research on TSA has been conducted and is public domain.

Time-series data are seldom linear, so the first objection does not make sense. Secondly, normality need not be assumed - this is a myth.

For those who'd like to learn more about TSA, two excellent introductions are:

- Time Series Analysis and Its Applications (Shumway and Stoffer)
- Time Series Analysis (Wei)

There are many others on specialized topics, such as:

- Multiple Time-Series Analysis (Lütkepohl)
- Time Series Analysis by State Space Methods (Durbin and Koopman)
- Time Series Modelling with Unobserved Components (Pelagatti)
- Hidden Markov Models for Time Series (Zucchini)
- GARCH Models (Francq and Zakoïan)
- Handbook of Volatility Models and Their Applications (Bauwens et al.)
- Nonparametric Econometrics (Li and Racine)

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