### Abstract

This chapter deals with the analysis of measurements over time, called time series analysis. Examples of time series include inflation and unemployment indices, stock prices, currency cross rates, monthly sales, the quarterly number of claims made to an insurance company, outstanding liabilities of a company over time, internet traffic, temperature and rainfall, and the number of mortgage defaults. Time series analysis aims to explain and model the relationship between values of the time series at different points of time. Models include ARIMA, structural, and stochastic volatility models and their extensions. The first two classes of models explain the level and expected future level of a time series. The last class seeks to model the change over time in variability or volatility of a time series. Time series analysis is critical to prediction and forecasting. This chapter explains and summarizes modern time series modeling as used in insurance, actuarial studies, and related areas such as finance. Modeling is illustrated with examples, analyzed with the R statistical package.

Language | English |
---|---|

Title of host publication | Predictive Modeling Applications in Actuarial Science |

Subtitle of host publication | Volume I : Predictive Modeling Techniques |

Editors | Edward W. Frees, Richard A. Derrig, Glenn Meyers |

Place of Publication | Cambridge |

Publisher | Cambridge University Press |

Pages | 427-448 |

Number of pages | 22 |

ISBN (Electronic) | 9781139342674 |

ISBN (Print) | 9781107029873 |

DOIs | |

Publication status | Published - 2014 |

### Fingerprint

### Keywords

- Finance and accountancy
- Finance and insurance
- Statistics for econometrics

### Cite this

*Predictive Modeling Applications in Actuarial Science: Volume I : Predictive Modeling Techniques*(pp. 427-448). Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9781139342674.017

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*Predictive Modeling Applications in Actuarial Science: Volume I : Predictive Modeling Techniques.*Cambridge University Press, Cambridge, pp. 427-448. https://doi.org/10.1017/CBO9781139342674.017

**Time series analysis.** / de Jong, Piet.

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Research › peer-review

TY - CHAP

T1 - Time series analysis

AU - de Jong, Piet

PY - 2014

Y1 - 2014

N2 - This chapter deals with the analysis of measurements over time, called time series analysis. Examples of time series include inflation and unemployment indices, stock prices, currency cross rates, monthly sales, the quarterly number of claims made to an insurance company, outstanding liabilities of a company over time, internet traffic, temperature and rainfall, and the number of mortgage defaults. Time series analysis aims to explain and model the relationship between values of the time series at different points of time. Models include ARIMA, structural, and stochastic volatility models and their extensions. The first two classes of models explain the level and expected future level of a time series. The last class seeks to model the change over time in variability or volatility of a time series. Time series analysis is critical to prediction and forecasting. This chapter explains and summarizes modern time series modeling as used in insurance, actuarial studies, and related areas such as finance. Modeling is illustrated with examples, analyzed with the R statistical package.

AB - This chapter deals with the analysis of measurements over time, called time series analysis. Examples of time series include inflation and unemployment indices, stock prices, currency cross rates, monthly sales, the quarterly number of claims made to an insurance company, outstanding liabilities of a company over time, internet traffic, temperature and rainfall, and the number of mortgage defaults. Time series analysis aims to explain and model the relationship between values of the time series at different points of time. Models include ARIMA, structural, and stochastic volatility models and their extensions. The first two classes of models explain the level and expected future level of a time series. The last class seeks to model the change over time in variability or volatility of a time series. Time series analysis is critical to prediction and forecasting. This chapter explains and summarizes modern time series modeling as used in insurance, actuarial studies, and related areas such as finance. Modeling is illustrated with examples, analyzed with the R statistical package.

KW - Finance and accountancy

KW - Finance and insurance

KW - Statistics for econometrics

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U2 - 10.1017/CBO9781139342674.017

DO - 10.1017/CBO9781139342674.017

M3 - Chapter

SN - 9781107029873

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EP - 448

BT - Predictive Modeling Applications in Actuarial Science

A2 - Frees, Edward W.

A2 - Derrig, Richard A.

A2 - Meyers, Glenn

PB - Cambridge University Press

CY - Cambridge

ER -