Time Series Analysis

A specific way of analysing a sequence of variables or data points collected over an equal time interval is called time series analysis. In this process, data are not recorded randomly; the analysts record the data over an interval of time. The time series is different from other data because the analysis shows how variables change over time. Several data points are required to ensure regularity and reliability in this process.

Several organisations use time series analysis to understand the fundamental causes of systemic patterns and trends over an interval of time. The time-series data can be used for predicting future data. In other words, it is the way of studying the characteristics of the variable with respect to time.

Time Series Analysis

The collection of observations obtained through repeated measurements over an interval of time is called time series analysis. The time series analysis is used to get meaningful statistics and other data characteristics. The ‘Time Series Analysis’ is distinct from other analyses because the ordering of observations is natural in this process. It is distinct from other analyses because the information typically relates to geographical location. It is used for things that are constantly fluctuating with time. Several industries use time series analysis because currency and sales are constantly changing. Finance, retail and economics industries use this type of analysis.

Some examples of time series analysis include:

• Measurements of rainfall
• Monitoring the heart rate (Electrocardiograph)
• Recording the readings of temperatures
• Recording the data of weather
• Analysis of stock prices
• Recording the quarterly sales

Components of time series analysis

The components of time series analysis are the reasons which affect the value of an observation. Its components are:

• Trends of time series analysis – During a long interval of time, the increase or decrease of the data shows the trend of time series analysis. The increase or decrease of observations in data is not always in the same direction in the given time period. The trend is a long-term, smooth and general tendency. Examples of trends are the number of deaths and births in a population, number of schools and colleges in an area, number of industries or factories in an area, etc. There are two types of trends:  linear and non-linear.
• Periodic Fluctuations – The fluctuations that repeat themselves over an interval of time are called periodic fluctuations. There are two types of periodic fluctuations:

1. Seasonal fluctuations – These variations show the same pattern over a period of twelve months. The seasonal data are recorded hourly, monthly, daily, quarterly, or annually in time series analysis.
2. Cyclic fluctuations – These variations show the same pattern during a period of more than one year. In time series analysis, the oscillatory movement has a period of oscillation of more than a year. Cyclic fluctuations are sometimes also called the ‘business cycle’.

• Random or irregular movements – The variation which is not regular or purely random is known as a random or irregular movement. Random movements are unpredictable or uncontrollable, like earthquakes, floods, disasters etc.
• Mathematical model of time series analysis – Mathematically, time series is expressed as Xt = f(t). Mathematical models of time series are of three types:

1. Additive model for time series analysis.
2. Multiplicative model for time series analysis.
3. A mixed model for time series analysis.

Types Of Time Series Analysis

The time series analysis includes variations of data. Its models of analysis include: 

• Classification – It categorises the data and identifies the data
• Curve fitting – The data is plotted along the curve to study the relationships within the data
• Descriptive analysis identifies the time series data like tends, seasonal variations, etc
• Explanative analysis – It understands the data and its relationship with it
• Exploratory analysis – It tells the characteristics of time series data
• Forecasting –  It predicts the future trend
• Intervention analysis – It describes how an event can change data
• Segmentation – It splits the data into two or more segments to show basic properties

Conclusion 

Identifying common patterns displayed over an interval of time is called time series analysis. Time series analysis is crucial and is commonly used for analysing the stock market, census, economic forecast, etc. It is performed to understand the basic structure and forces of the observed data. 

The goal of time series is to predict the future; time acts as an independent variable in time series analysis. It determines a model that can be used for sales, business, stock market price etc. There are four components of time series: its trends, periodic functions, random or irregular movements, and mathematical variations. It is one of the most common data types used in everyday life.