Time has always been a crucial factor when we record or collect data. And in time series analysis, time is a vital variable of the data. Time series analysis helps us to study the progress over a period of time.

What is the time series analysis?

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

The analysis is done on the basis of previously observed values and intervals.

For example- Forecasting the sales of electronic items during Christmas based on the last 6 years of festive season sales.

However, time series analysis is not merely the act of collecting data over time. Time series analysis can show how variables change over a certain period of time. Time series data helps predict and forecast future data on account of historical data.

Why do organizations use time-series data analysis?

organizations use time series analysis to understand the systemic patterns and underlying causes of trends over time.

Using data visualizations, business users can dig deeper into why these trends occur.

By analyzing the data over consistent intervals, organizations can predict the likelihood of future events. It can show changes like cyclic behaviour or seasonality, which gives a better understanding of data variables and helps forecast better.

For example, Dell analyzed five years of festive season sales data to identify the right strategy for this festive season.

Uses of time series analysis

Time series analysis is usually used in industries where there are fluctuations over time or are affected by time. Industries like retail, E-commerce, and finance often use time series analysis because currency and sales are always changing.

Stock market analysis is an outstanding example of time series analysis, especially with automated trading algorithms. Furthermore, time series analysis is also used to forecast weather changes, helping meteorologists predict the weather from the last few days of the weather report.

Few applications of time series analysis include:

• Weather data
• Rainfall measurements
• Temperature readings
• Heart rate monitoring (EKG)
• Brain monitoring (EEG)
• Quarterly sales
• Stock prices
• Automated stock trading
• Industry forecasts
• Interest rates

Time Series Data Components

The time-series data will include seasonality, trends, noise or randomness, a curve, and the level. Before we define these terms, it’s important to note that not all time series data will include all of these time series components.

Here are the components that can occur in time series data:

Level: The “level” or the “level index” of time series data refers to the mean of the series.

Noise: Time series data will always have some noise or randomness in the data points that aren’t associated with any defined trends.

Seasonality: There are regular seasonal predictable fluctuations in the series. They could be quarterly, weekly, or even days of the week. It’s important to note that seasonality is domain-specific.

For example- Books sales significantly increase during April and May. Another solid example is electronic item sales are usually higher during festive days compared to normal days.

Trend: Upward & downward movements of the data over a particular period of time. Data has a long-term trajectory that can either be trending in a positive or negative direction. An example of a trend would be movements in the stock market.

Cycle: Repeating changes that are not correlated to the calendar. This includes business cycles such as economic downturns or expansions or salmon run cycles but isn’t related to the calendar in the weekly, monthly, or yearly sense.

Primary techniques and tools for time series analysis 

Just as there are many types, components, and models in time series analysis, there are also a variety of methods/tools to study data. Here are the three most common.

1. Box-Jenkins ARIMA models: These models are used to understand a single time-dependent variable, like temperature over time, and predict future data points of variables. These models assume that the data is stationary. Analysts have to remove as many differences and seasonality in past data points as they can. Thankfully, the ARIMA model includes terms to account for seasonal difference operators, moving averages, and autoregressive terms within the model.
2. Box-Jenkins Multivariate Models: These models are used to analyze more than one time-dependent variable, such as temperature and humidity, over time.
3. Holt-Winters Method: The Holt-Winters method is an exponential smoothing technique. It is designed to predict outcomes from the data points that include seasonality.

Conclusion

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.

Events closed together in time often have a stronger connection than more distant findings.

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