What is time series analysis?

Time series analysis is a statistical technique used to analyze and interpret data collected over a time period at regular intervals. This analysis is commonly done in various fields such as finance, economics, weather forecasting, stock market analysis, etc.

Procedure

For example, we have time series data of the daily average temperature of a city. A snippet of the data is shown below:

The major components involved in the time series analysis are as follows:

Trend

The trend component represents the time series's underlying long-term direction. It captures the gradual and systematic change in the data over an extended period.

Trends can be of various types:

• Upward trend: The data shows a consistent increase over time, indicating growth or positive development.

• Downward trend: The data shows a consistent decrease over time, indicating a decline or negative development.

• Flat trend: The data remains relatively constant over time, indicating stability.

The trend component helps us understand whether the time series increases, decreases, or stays relatively constant.

Seasonality

Seasonality captures the repeating patterns with a fixed frequency within the time series. These cycles may be daily, weekly, monthly, or yearly, depending on the nature of the data.

Residual

The residual component, also known as the irregular component or noise, represents the random fluctuations or erratic behavior of the data in the time series that cannot be attributed to the trend or seasonal patterns.

Conclusion

Time series analysis is a powerful tool that makes informed predictions from historical data to make better decisions and strategies for the future. The above mentioned components are the building blocks of time series analysis. We can apply many other functions in our analysis, such as autocorrelation and ARIMA.