Tableau Time Series Analysis Simplified

Time Series Analysis is an integral step towards a better understanding of data. Time Series Analysis grants you the ability to peruse through your data, drilling down into it from years to days. This helps you extract actionable insights over different periods for an in-depth analysis. Tableau is a tool that lets you leverage Time Series Analysis to analyze important trends. Tableau Time Series Analysis lets you utilize its drag-and-drop feature to evaluate times by day of the week, drill down with a click and perform time comparisons like Moving Averages and Year-Over-Year growth easily.

In this article, you will learn about Tableau Time Series Analysis in great detail, and how you can implement Tableau Time Series Analysis for an exhaustive view of your customer data. It explores the importance of Time Series Analysis, types, and models that can be put to use, a few applications of Tableau Time Series Analysis, and wraps up with how you can set up a Tableau Time Series chart.    

Introduction to Tableau

Tableau is a Business Intelligence tool that is commonly leveraged to empower the Analytics pipeline with its elegant visualizations. It houses a very intuitive, easy-to-grasp user interface that allows users even from non-technical backgrounds to extract valuable insights as well. Apart from this, Tableau is best known for Real-Time Analysis, Data Blending, and Collaboration.

Here are a few benefits of leveraging Tableau as a part of your Analytics pipeline:

• High Performance
• Mobile Friendliness
• Ease of Use
• Multiple Data Source Connections
• Thriving Community and Forum 

Tableau is a product suite that consists of the following products:

• Tableau Public
• Tableau Desktop
• Tableau Online
• Tableau Reader
• Tableau Server

The products in the Tableau product suite can be broadly classified into two types:

• Sharing Tools: These are the tools used for sharing the reports, dashboards, and visualizations created using the developer tools. Tableau Online, Tableau Server, and Tableau Reader are all examples of Sharing Tools. 
• Developer Tools: These tools are used for development. This includes the creation of dashboards, report generation, charts, and visualization. Tableau Desktop and Tableau Public are examples of Developer Tools.   

Here are a few key applications of Tableau:

• Data Visualization
• Data Collaboration
• Business Intelligence
• Data Blending
• Real-Time Data Analysis
• Importing Data (Large Size)
• Query Translation to Visualization
• Creating No-code Data Queries   

Introduction to Time Series Analysis

Time has been a crucial factor for recording data right from the start. Time Series Analysis allows you to analyze a sequence of data points collated over a specified time duration. Here, analysts record data points at congruous intervals of time, instead of randomly or intermittently.

Time Series Analysis stands apart because it gives users a holistic understanding of how variables change over time. It acts as a supplementary source of information and offers a set order of dependencies between the data. The primary requirement for Time Series Analysis is a large number of data points for reliability and consistency. This ensures that any patterns or trends discovered aren’t outliers borne from seasonal variance.

Understanding the Importance of Time Series Analysis

As seen in the previous section, Time Series Analysis can lend a helping hand to the organizations trying to understand the cause of certain systemic patterns or trends over time. By leveraging a Data Visualization tool like Tableau, you can utilize its visualization capabilities to explore and understand the underlying reason behind seasonal trends. 

Time Series Analysis can also be leveraged to predict future outcomes based on historical data. This is a part of Predictive Analytics that can depict the likely changes in the data namely cyclic behavior or seasonality. For instance, Des Moines Public Schools leveraged Tableau Time Series Analysis to analyze the achievement data of students dating back five years. This allowed them to identify the at-risk students and evaluate their progress over time.

Tableau Time Series is mainly used for non-stationary data, i.e. things that are in a state of constant flux or affected by time. Tableau Time Series Analysis is widely used by the Retail, Finance, and Economics industries due to the dynamic of currency and sales. Stock Market Analysis with automated trading algorithms is a very good example of Tableau Time Series Analysis. Here are a few other applications of Time Series Analysis:

• Rainfall Measurements
• Weather Data
• Heart Rate Monitoring (EKG)
• Quarterly Sales
• Brain Monitoring (EEG)
• Temperature Readings
• Interest Rates

Tableau Time Series Analysis sometimes warrants the creation of complex models due to the presence of numerous data variations in data. However, analysts cannot account for every data variation, or build a general model that works for every use case. Underfitting or Overfitting the models leads to the models not being able to differentiate between a genuine random error and true relationships. This results in skewed analyses and incorrect forecasts. In the next section, you will look at a few things you can keep in mind to prevent a skewed analysis.

Understanding the Types of Time Series Analysis

Time Series Data can be broadly classified into two types:

• Flow Time Series Data: This type of Time Series Data is associated with measuring the activity of the attributes over a certain duration. This is a part of the whole measurement and makes up a portion of the results. 
• Stock Time Series Data: For Stock Time Series Data, you measure the attributes at a given point in time, not over a duration. 

Before touching upon the types of Time Series Analysis and helpful models that you can use to get accurate results, you need to understand the types of data variations that can occur. The following variations can occur sporadically throughout the data:

• Analysis of Trends: Trend Analysis allows you to identify congruous movement in a certain direction. You can either determine the underlying cause defined as deterministic trends or stochastic trends that are inexplicable.
• Seasonal Variation: These events occur at specific and regular intervals in a year. When data points close together in time, tend to be related, serial dependence takes place. 
• Functional Analysis: In Functional Analysis, you can pinpoint the relationships and patterns within the data to identify noteworthy events.

After the analysts have decided the types of data relevant to the business question they are trying to answer, they can decide the types of analysis and models to achieve the best results. The different types of Time Series Analysis are as follows:

• Explanative Analysis: This type of Time Series Analysis attempts to understand the relationships within the data, the cause, and the effect on them. 
• Descriptive Analysis: In Descriptive Analysis, you can identify patterns in Time Series data like cycles, trends, or seasonal variation.
• Exploratory Analysis: The primary characteristics of Time Series data are highlighted in a visual format in Exploratory Analysis.
• Forecasting: As is evident from the name, predicting future data is Stamford Health’s problem based on historical trends. It uses historical data as a basis for building a model to predict scenarios that might happen in the future.
• Curve Fitting: Here the data is plotted along a curve to assess the relationships of variables within the data.
• Classification: Classification deals with the identification and designation of categories to the data.
• Segmentation: Segmentation splits the data into sections/fragments to reveal properties of the source information.
• Intervention Analysis: Intervention Analysis deals with the study of the changes an event can bring to the data.

Understanding the Models of Time Series Analysis

Several methods and models exist that can help you study data. Here are the three most commonly used models and methods to help you gain better insights:

• Box-Jenkins Multivariate Models: These models are used to evaluate multiple time-dependent variables. For instance, measuring temperature and humidity over time. 
• Holt-Winters Method: The Holt-Winters Method is an exponential smoothing technique. This is used primarily to predict outcomes given data points displaying seasonality. 
• Box-Jenkins ARIMA Models: These models deal with a single time-dependent variable. The Box-Jenkins ARIMA Models are built on stationary data. This means analysts need to do away with as many differences and seasonality occurrences in historical data as they can. This might sound like a very complex idea, but ARIMA Models contain terms that take care of Seasonal Difference Operators, Moving Averages, and Autoregressive Terms in the model.

Tableau Time Series Analysis Applications

As seen before, time is an exceptionally critical variable for various industries, which means that Tableau Time Series Analysis has several applications. In the following sections, you will be looking at a few case studies that reflect the benefits reaped by each company in question by leveraging Tableau Time Series Analysis:

• Stamford Health
• Exelon
• Texas Rangers
• Bronto Skylift

1. Stamford Health

The problem Stamford Health tried solving using Tableau Time Series Analysis was inflated costs of care and operations over time and inefficient use of resources. Stamford Health used Tableau Time Series Analysis to identify areas of opportunity for the betterment of patient care and reduced costs for the system and the patients. The company used historical data regarding treatments, length of patient stays and conditions data to chart, and many more. These were used by Stamford Health to identify the apt/ideal times for the administration of medicines and consequently reduce the length of patient stays. This ended up reducing both patient and hospital costs.

In the medical field, Tableau Time Series Analysis is being widely used for chronic disease research and epidemic-scale research. It is also being used to track a patient’s heart rate through an electrocardiogram (EKG) or brain waves through an electroencephalogram (EEG). These devices record data over a period, which can identify any abnormal activity indicative of a medical issue.     

2. Exelon

Exelon used Tableau Time Series Analysis to overcome the problem of time-consuming traditional audits as they did not add much value to their business growth. Audits comprised interviewing the administrators or counselors who owned the processes and noting down the records at the time of the audit. Exelon took it a step further, and leveraged Tableau Time Series Analysis to evaluate an entire year of data and unearth trends that might have gone unnoticed the first time around.

Stock Prices are reported over a duration of time and this reporting also involves Tableau Time Series Analysis. Autonomous trading algorithms collect data on a market that keeps changing in accordance with minuscule financial changes and trade accordingly.

Tableau Time Series Analysis also comes in handy in budget analysis, sales forecasting, interest rates, financial markets, trend analysis, and seasonality to name a few. But Time Series Analysis of financial data can often include various variations which would need highly complex models to understand.    

3. Texas Rangers

Texas Rangers used Tableau Time Series Analysis to carry out Data Analysis at a faster pace which would give them enough time to make crucial decisions before game day. The front-office team of Texas Rangers unified all their data from all sources to ensure a 360-degree view of data. By using advanced dashboards, the Sales and Marketing teams used Tableau Time Series Analysis and other Data Analytics strategies to identify key opportunities mainly focused on forecasting against seasonal trends.

In one particular instance, the Texas Rangers Sales team looked at up-to-date dashboards to realize that their estimated Sales for an upcoming game were lower than anticipated. The Sales team put together a strategy around a week before the game to bolster ticket sales. They created a promotional Father’s Day ticket offer that helped them increase sales.   

4. Bronto Skylift

Bronto Skylift was dealing with time-consuming operations, manufacturing, and inaccurate sales forecasting which meant their sales were taking a serious hit. Bronto Skylift leveraged Tableau Time Series Analysis to cut down the time it was taking them to analyze the data from a day to an hour. By using Forecasting Modeling and Tableau Time Series Analysis, the company was able to predict supply chain and processes in the manufacturing department along with seasonal trends. Before Bronto Skylift decided to invest in Data Analytics, the data collated was siloed and stale. With these changes, Bronto Skylift managed to make forecasting more accurate, cut down the costs in inventory, labor, supply chain, and capital equipment.     

Setting Up a Tableau Time Series Chart

A Tableau Time Series chart can be set up by using the built-in time and date functions. These allow you to use the drag-and-drop option to analyze various trends, with the ability to drill down with a click and perform trend analysis comparisons easily. Now that you have looked at the various aspects of a Tableau Time Series, here is a look at a few simple steps you can follow to set up a Time Series chart in Tableau:

• Step 1: The first step is to drag the Order Date field and the Sales variable to the Columns and the Rows shelf respectively. The default chart provided is a yearly trend line chart. The Marks shelf in the chart selects a line graph for the chart by default.
• Step 2: The chart in step 1 displays the data on a yearly basis. To drill down to monthly and quarterly levels, you can click the plus icon on the Order Date field on the columns shelf. This allows you to display data on a monthly or quarterly basis respectively.
• Step 3: To display a chart in a more continuous format, you need to convert the chart you have right now to a continuous format Time Series chart. Start with rolling up the YEAR (Order Date) to year level. Next, right-click on it and select the Continuous and Year options. This is depicted as follows:

• Step 4: This is an alternative to the third step, wherein you can select the line chart type in the Show Me card, as depicted in the chart below:

You can also change the path property and the chart type as well in a Tableau Time Series chart to indicate the best trend shifts.

Conclusion

In this article, you learned about Time Series Analysis in explicit detail. This included covering the importance of Time Series Analysis, the different types of Time Series Analysis, and the models you can use to make the process of extracting insights more accurate. The article wraps up with a few case studies of the application of Tableau Time Series Analysis to improve efficiency and boost business growth. 

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