What is Time Series Analysis?

data analysis

Definition– Time Series is a series of observations taken at specific time intervals to determine the long-term trends and to forecast the future and sometimes to perform a few other analyses.

Time series analysis helps us to predict future trends on the basis of previously observed values.

For example- Forecasting the summer sales of a clothing brand on the basis of traffic and conversions they received in last year’s summer.

Uses of Time Series?

Time series analysis is used to see how a given product or variable changes over time. Time series data can be analyzed to extract essential statistics and other characteristics. It is commonly used in these four scenarios:

A) Business Forecasting

B) Understand Past Behavior

C) Planning the Future

D) Evaluating the Current Accomplishments

A) Business Forecasting:

One of the major uses of time series analysis is to forecast the business growth, using various tools, and techniques to predict various variables like sales, expenditures, and profits. Business forecasting helps to develop better strategies based on these informed predictions.

B) Understand Past Behavior:

Time series analysis is also used to understand past trends, outcomes, and customer behavior. The main purpose of doing this is to understand past mistakes as well as best practices. So that we could find out what worked and what didn’t.

C) Planning the Future:

Time series analysis has a significant role in the future planning of a business. By analyzing past values and trends we can make strategies for future endeavors.

D) Evaluating the Current Accomplishments:

We can also evaluate current, and past accomplishments using time series analysis. Analyzing our achievements helps us in performance management as well as human resources management. 

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Components of Time Series?

There are majorly four components of time series analysis:

A) Trend- Upward & downward movements of the data over a particular period.

For example- Movements in the stock market.

B) Seasonality- Seasonal variances represent the seasonal changes over a period.

For example- Book’s sales significantly increase during April and May.

C) Noise or Irregularity – Spikes & troughs happen at random intervals. There are no such variables to detect the movement of the trend. This component is almost unpredictable.

D) Cyclicity – Trends that repeat after a large interval of time, like months, years, etc.


Time series analysis tries to understand changes in patterns over time. These patterns help to generate precise forecasts, such as future sales, GDP, and global temperatures.

One thing to remember is that the time series models incorporate the fact that time flows in one direction.

Past events can influence future observations but not the other way around. Moreover, events closed together in time often have a stronger connection than more distant findings. While these ideas are obvious to us, statisticians had to build them into how these models work.

Like all data, time-series data contain random fluctuations. This randomness can obscure the underlying patterns. Smoothing techniques cancel out these fluctuations to unveil the trends and cycles more clearly.