Time series analysis is a statistical technique used to record and analyze data points over a period of time, such as daily, monthly, yearly, etc. A time-series chart is the graphical representation of the time series data across the interval period.
In time series analysis, you can record data points at consistent intervals over a set period of time instead of just recording the data points randomly.
The time provides an additional source of information and set an order of dependencies between the data. The time series analysis requires a large number of data to ensure its consistency and reliability.
Why use Time-series data analysis:
Time series analysis helps you to understand the underlying causes of trends or systematic patterns over time. Using data visualizations, business users can see seasonal trends and dig deeper into why these trends occur.
When organizations analyze data over consistent intervals, they can also use time-series forecasting to predict the likelihood of future events.
Time series forecasting is part of predictive analytics. It can show likely changes in the data, like seasonality or cyclic behavior, which provides a better understanding of data variables and helps forecast better.
Today’s technology allows us to collect massive amounts of data every day and it is easier than ever to gather enough consistent data for comprehensive analysis.
When to use Time series analysis and when not to use:
The Time series analysis is not new, but today's technology makes it easier to access. the Time series analysis is used for non-stationary data, which means the things that are not affected by time.
And industries like retail, finance, and economics will frequently start using time series analysis because the currency and sales are always kept changing.
Stock market analysis is an excellent example of time series analysis in action, especially with automated trading algorithms.
Likewise, time series analysis is ideal for forecasting weather changes, helping meteorologists predict everything from tomorrow’s weather report to future years of climate change.
Examples : Weather data, Rainfall measurements, Temperature readings, Heart rate monitoring (ECG), Brain monitoring (EEG), Quarterly sales, Stock prices, Automated stock trading, Industry forecasts, Interest rates.
Classification of Time series :
There are different types of data that describe how and when that time data was recorded.
- Time series data is data that is recorded over consistent intervals of time.
- Cross-sectional data consists of several variables recorded at the same time.
- Pooled data is a combination of both time-series data and cross-sectional data.
Types of time series analysis:
- Classification : Identifies and assigns categories to the data.
- Curve fitting : Plots the data along a curve to studying the relationships of variables within the data.
- Descriptive analysis : Identifies patterns in time series data, like trends, cycles, or seasonal variation.
- Explanative analysis : Attempts to understand the data and the relationships within it, as well as cause and effect.
- Exploratory analysis : Highlights the main characteristics of the time series data, usually in a visual format.
- Forecasting : Predicts future data. This type is based on historical trends. It uses historical data as a model for future data, predicting scenarios that could happen along with future plot points.
- Intervention analysis : Studies how an event can change the data.
- Segmentation : Splits the data into segments to show the underlying properties of the source information.
The Tableau provides many options to build time -series charts. The built-in date and time functions allow you to use the drag-and-drop option to create and analyze time trends, drill down with a click, and easily perform trend analysis comparisons.
Here, I am using a Sample Superstore file to build a time series chart.
The following example demonstrates the Time series analysis:
- Open Tableau and click on Connect to Data.
- Then the Connect page will open, click on Microsoft Excel.
- Navigated to the downloaded files, select P1-OfficeSuppliesxls excel file, which is a saved excel file, and click on Open.
- You can download a Sample Superstore.xls excel file from the link: https://chercher.tech/files/
- Once the data source has been connected, open a new worksheet by clicking on sheet1.
- Next, click on sheet1 and then drag the order date to the column shelf and the sales variable to the rows shelf.
- The default chart gives us a yearly trend line chart and the Marks shelf automatically selects the line graph for the chart as shown below.
- By clicking on the + symbol on the column shelf near the Order date, will display the line graph for quarter level as below.
- The Image shows the graph broken into the month level.
- The above image will show the graph in a discrete format, but it will be helpful if it will be displayed in a continuous format.
- To convert the chart into a continuous format time series chart, the first step is to:
- Roll up the YEAR (Order Date) back to the year level.
- The second step is to right-click on Year(Order Date) and select the Year and Continuous options.
- Now you can see the continuous line graph.
- It is easy to change the chart breakdown from annual to monthly level.
- This can be done by simply changing the Columns shelf from YEAR (Order Date) to MONTH (Order Date).
- This will generate a monthly time series chart.
- From an analytics perspective, this chart is more insightful as it allows us to see the sales fluctuations across months and years.
- It is also helpful for decomposing the seasonality and trend components of the time-series data.
- Tableau also provides the option to change the type of line graph.
- By clicking on the path in the Marks shelf, we will get different options for line graphs.
- We can also change the type of chart to either bar graph, area graph, or others by selecting the options from the drop-down list in the Marks shelf.