Time Series Analysis and Its Uses

Time Series Analysis And Its Uses

Time series analysis is a defined way of interpreting several data collected over a specific period. An organised method of answering the mathematical and statistical queries raised by time correlations is called time series analysis. Time is a variable, and the objectives of time series analysis are to record the abundant history data over a large period, helping in future predictions. 

Data which are affected or influenced by time besides other variables are time series data. Organisations understand the trend shift over time and equip themselves accordingly. A career in data science is one of the most lucrative careers in the modern day. A data analyst has the knowledge of how to analyze time series, gathers and extrapolates these data, and captures meaningful insights. 

Types of Time Series Analysis

There are different types of time series data analysis and each type has its characteristics. The three types of time series analysis are as follows - 

Exploratory analysis 

Exploratory analysis is followed by a good quantum of available data and the process explains the trend of the time series itself.

Curve fitting 

When we wish to deduce unknown data from other known or available data, we need to fit the available data into a curve. Such a curve, for which one of the variables is time, helps us to locate the unknown number for a specific time. 


This is another advance from time series data analysis which extrapolates regression models. The process of prediction gets inputs from artificial intelligence and machine learning.

Time Series Analysis Models

The following models are the most common time series analysis models –

  • Box-Jenkins ARIMA Models
  • Box-Jenkins Multivariate Models
  • Holt-Winters Method

To be a master of these models a prospective candidate needs to complete a data analytics course from a reputed institute. 

Data Classifications

Time series analysis data may be classified into two broad categories  - 

  • Stock time series data, which essentially records parameters at a specific point in time 
  • Flow time data series, which collects information over a reasonable period, which is part of the total time cycle. 

Data Variations

Variations may occur randomly in a time series analysis data. Functional analysis is applied where data is a function of time and has a relationship with each other. Trend analysis captures data following a unique direction and the trend may be random. Seasonal variations in data are also observed cyclically, at a specific time of the year. 

Data Science Course

Applications of Time Series Data 

Time series analysis data are particularly useful in the fields of economics, medicine, social and environmental sciences. A data analyst course is a must to become an expert in gathering, filtering and extrapolating data depending on the objectives of time series analysis

The time series analysis examples and applications are as follows - 

In the financial domain - Forecast in finance is a function of statistical data modelling. Time series helps in predicting both short-term and long-term financial goals.

In the medical treatment domain - Doctors and health care units refer to huge amounts of history data, about patients’ reports and their subsequent outcomes following the medical process. Thus, the prediction of an outcome on the application of a specific treatment can be made by using historical data/case studies. 

In the medical equipment domain - Medical practitioners, nowadays, rely on medical equipment that deploys time series data for the diagnosis of diseases or performing preventative treatment on patients. In this process, medical practitioners follow trends which have been established by following the history data from this equipment for specific cases.

In the astronomical field - The oldest usage of time series data in history has been observed in this field. Cases of sunspot data collection have been recorded as early as 800 BC in China and from then, it has become a well-recorded natural phenomenon. Regular observation of the movement of special bodies concerning time has created avenues for scientists to deduce distances between them depending on the intensities and wavelengths of light.

In forecasting the weather - Weather study has been in practice since ancient days. However, the use of barometers to record weather data started at a much later stage. Now, sophisticated equipment can record all weather parameters like temperature, air pressure, humidity, and air pollution level at any location on Earth and can also forecast hourly or daily weather. All this has been made possible by the usage of time series data accumulated over a period.  

In business development - Business forecasting cannot be done by experience alone. Informed business decisions based on data time series data. These large data are reliable and reveal the growth trend of the business. Estimation and seasonal patterns of a specific business are also revealed. 

Challenges in Managing Time Series Data

The challenges in managing the time series data are as follows - 

  • Missing data is a serious source of problems. This may be handled by applying interpolation or imputation after analysing the extent of missing figures.
  • Overcoming noise or random data fluctuations is also a major roadblock since it disturbs the set trend. Data analysts apply proven filtering techniques like moving averages or wavelet transforms to reduce unwarranted fluctuations and gather suitable information from data.


A career in data science is attracting young minds from backgrounds other than science and engineering. The prospective candidate must have a deep interest in statistics and mathematics. The understanding of logical reasoning will be an additional advantage to the candidate. A data analyst course from an esteemed institution shall take care of the rest.

The career of a candidate who has completed a data science course is also rewarding in terms of compensation. The knowledge of how to analyze time series, choice of filtered data and a proper methodology matters when creating an accurate prediction model. Business houses require a reliable sales forecast of their existing products. Such serious predictive information is one of the time series analysis examples that may be derived by using the right data and a proper analysis model and technique. 

A data analytics course or a data science course is offered by many reputed institutions nowadays. Candidates may choose either of them and boost up a modern-day career. 

The Postgraduate Program in Data Science and Analytics offered by Imarticus is one of India’s best data science and analytics courses. The duration of the program is 6 months. Both online and offline modes of teaching are available. 

Visit the official website of Imarticus for more details.

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