Time Series

What is Time Series analysis?

History and Definition

Time Series is a sequence of well-defined data points measured at consistent time intervals over a period of time. Data collected on an ad-hoc basis or irregularly does not form a time series. Time series analysis is the use of statistical methods to analyze time series data and extract meaningful statistics and characteristics about the data.

Time series Analysis helps us understand what are the underlying forces leading to a particular trend in the time series data points and helps us in forecasting and monitoring the data points by fitting appropriate models to it.

Historically speaking, time series analysis has been around for centuries and its evidence can be seen it the field of astronomy where it was used to study the movements of the planets and the sun in ancient ages. Today, it is used in practically every sphere around us – from day to day business issues (say monthly sales of a product or daily closing value of NASDAQ) to complicated scientific research and studies (evolution or seasonal changes).

Benefits and Applications of Time Series Analysis

Time series analysis aims to achieve various objectives and the tools and models used vary accordingly. The various types of time series analysis include –

  • Descriptive analysis – to determine the trend or pattern in a time series using graphs or other tools. This helps us identify cyclic patterns, overall trends, turning points and outliers.
  • Spectral analysis – is also referred to as frequency domain and aims to separate periodic or cyclical components in a time series. For example, identifying cyclical changes in sales of a product.
  • Forecasting – used extensively in business forecasting, budgeting, etc based on historical trends
  • Intervention analysis – is used to determine if an event can lead to a change in the time series, for example, an employee’s level of performance has improved or not after an intervention in the form of training – to determine the effectiveness of the training program.
  • Explanative analysis – studies the cross correlation or relationship between two time series and the dependence of one on another. For example the study of employee turnover data and employee training data to determine if there is any dependence of employee training programs on employee turnover rates over time.

The biggest advantage of using time series analysis is that it can be used to understand the past as well as predict the future. Further, time series analysis is based on past data plotted against time which is rather readily available in most areas of study.

Time Series analysis chart

For instance, a financial services provider may want to predict future gold price movements for its clients. It can use historically available data to conduct Time series analysis and forecast the gold rates for a certain future period.

There are various other practical applications of time series analysis including economic forecasting, census analysis and yield projections. Further, it is used by investment analysts and consultants for stock market analysis and portfolio management. Business managers use time series analysis on a regular basis for sales forecasting, budgetary analysis, inventory management and quality control.

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