Preface

A time series is simply a sequence of data points occurring in succession for a given period of time. Time series data are a collection of observations obtained through repeated measurements over time. Time series data are of two types: measurements gathered at regular time intervals (metrics); and measurements gathered at irregular time intervals (events).

Time series data are everywhere since time is a constituent of everything that is observable. As our world becomes increasingly digitized, sensors and systems are constantly emitting a relentless stream of time series data which have numerous applications across various industries. Graphs of time series data points can often illustrate trends or patterns in a more accessible, intuitive way.

Time series analysis is a specific way of analyzing a sequence of data points that are collected and recorded at consistent intervals over a set period of time rather than just recorded intermittently or randomly. Time series analysis typically requires a large number of data points to ensure consistency and reliability. It also ensures that any trends or patterns discovered are not outliers and can account for seasonal variance. Additionally, time series data can be used for forecasting and predicting future data based on historical data. Time series analysis is used for non-stationary data, things that are constantly fluctuating over time or are affected by time. Industries like finance, retail, and economics frequently use time series analysis. New time series analysis tools are needed in disciplines as diverse as astronomy, economics, and meteorology. Examples of time series analysis presented in this book include:


Among other applications of time series analysis are quarterly sales, weather forecasting, rainfall measurement, heart rate monitoring (EKG) and brain monitoring (EEG).

Because time series analysis includes many categories or variations of data, analysts must sometimes make complex models. However, not all variances can be accounted for, and models that are too complex or that try to do too many things can lead to a lack of fit. Lack of fit or overfitting models may fail to distinguish between random error and true relationships, leaving analysis skewed and forecasts incorrect.

Time series analysis models include the following types:


There are a few factors that can cause variations in time series data. The following five components are used to describe how time series data behaves:


**Rifaat Abdalla** Department of Earth Sciences,

College of Science, Sultan Qaboos University, Al-Khoudh, Oman

### **Mohammed El-Diasty**

Sultan Qaboos University, Al-Khoudh, Oman

## **Andrey Kostogryzov and Nikolay Makhutov**

Russian Academy of Sciences, Moscow, Russia

Section 1

Time Series

Data Modeling

**1**

Section 1
