It’s a great time to be in the data analytics field. By 2031, the employment rate for data scientists and statisticians is expected to increase by 36% and 33%, respectively – enough to secure top-ten rankings for both professions on fastest-growing professions list from the U.S. Bureau of Labor Statistics.
Nested within the field of data analytics, time series analytics is key to understanding how data changes over time to make accurate predictions about the future. (The term “time series forecasting” is often used interchangeably with “time series analytics” because the two are so deeply intertwined).
If your data set includes time stamps, you have the option of leveraging time series analytics. Introducing time as factor lets you identify the following elements:
- Trend: A trend is a consistent upward or downward movement in your time series. Trends aren’t always immediately explainable, so if you spot one, your work isn’t necessarily done – you’ll also want to see if you can find the trend’s cause. If you can identify a cause, you’ve found a deterministic trend. If you can’t identify a cause, it’s a stochastic trend.
- Seasonality: If your trend reappears at the same time(s) of the year every year, then it’s seasonal. Seasonality is an especially big factor for retail businesses because many products have demand that fluctuates with the seasons – when it’s cold outside people want sweaters, not pool supplies.
- Cycle: Apparently flare-legged jeans are popular with the kids these days. The last time the jeans were popular, it was the early 2000’s; before that, the 1970’s. This is known as a cycle: a trend that repeats itself at periodic intervals, but there’s not a time pattern between each cycle to help us predict when the next one will come. We may not be able to predict exactly when the next cycle will hit, but we can spot its earliest signs and predict how it will play out.
- Irregularity: When you remove the trends from your data set, you may still notice unexpected spikes at random, unpredictable times. This is known as an irregularity. It can essentially be considered noise.
The ability to explore trends and make forecasts is crucial to many fields. Just how many use cases are there? Too many to count – but the 20 listed below can help you get the gears turning about the possibilities.
20 Use Cases for Time-Series Analytics
- Marketing campaign analysis: Marketers use data all the time to measure the effectiveness of their efforts – but data by itself often can’t provide a complete picture. Factoring in seasonality and other trends helps marketers understand what’s influencing sales outside of the campaign. Nobody wants lackluster sales to be attributed to a failed marketing campaign when the real culprit is seasonality.
- Demand and/or trend forecasting: Time series analytics can help you forecast future sales performance based on your past sales data. This can help you plan your inventory, staffing, budget, and promotions throughout the year.
- Customer behavior analysis: On what day(s) and time(s) are people more likely to open that email? Use social media? Browse your website? If you can find patterns in customer behavior, like purchasing habits or website use, you can optimize your marketing and customer support resources for peak activity.
Transportation & Utilities
- Traffic pattern analysis: Time series analytics lets you analyze patterns in traffic flow and congestion over time and identify the factors that influence them. For transit planners and engineers, this is an important way to design more efficient roads and public transit systems.
- Schedule optimization: Just like retail businesses, transportation companies must understand when their high-demand times, days, and seasons are so they can meet the demand (in this case, by providing more routes during those times).
- Maintenance: Analyzing the patterns in maintenance needs and downtime for manufacturing equipment can help manufacturers schedule their maintenance and downtime more effectively.
- Production optimization: You can also analyze patterns in production to identify bottlenecks and inefficiencies, helping manufacturers make their processes more efficient.
- Stock market analysis and prediction: Forecasting stock prices based on historical data is critical for understanding when to buy and sell stocks. Long-term trends, short-term trends, macro trends, and cycles all influence whether a given stock is a buy, a sell, or a hold – and for how long.
- Risk analysis: The same methods can be used to assess stock price volatility, helping you understand the risk associated with various investments.
- Asset allocation: Patterns in the returns of different asset classes like stocks, bonds, and commodities can help you optimize the mix of assets in an investment portfolio, giving you a better chance of maximizing your return and minimizing risk.
- Student performance: Time series analytics can help you identify the factors that may impact student performance over time. This information is crucial for early intervention because you can help students the instant they trigger a warning signal for a recognized pattern or cycle.
- Enrollment: Schools and universities can use their historical enrollment data to forecast and plan for future enrollment.
- Educational trends: Time series analytics can also help you stay on top of educational trends, like the number of students studying a particular subject or favoring a certain learning environment (in-person, remote, asynchronous, etc). Schools can then make sure they’re providing the programs and learning options students are looking for.
Weather & Environment
- Weather forecasts: Weather forecasts rely on historical data to make predictions, which is the reason they can be hit-or-miss. The closer they are to the data in question, the more data they have and the more accurate their predictions will be. That’s why most of us look at the weekly or daily forecasts to plan our trips to the beach.
- Climate change analysis: The patterns in temperature and other climate data over time help environmentalists chart climate change and identify connected trends and cycles.
- Natural disaster preparedness: Natural disasters like hurricanes and earthquakes are also cyclical, so you can use a natural disaster’s historical data to forecast when the next one is due to occur and prepare accordingly.
Disease Forecasting & Other Macro Trends
- Disease forecasting: Public health officials can plan for and respond to disease outbreaks using historical data on the transmission rates and seasonality of diseases like influenza. One of the reasons officials were slow to understand COVID-19 was because they didn’t have historical data to predict how the disease would play out.
- Economic forecasting: Historical data can also be used to forecast economic factors like GDP, inflation, and unemployment rates.
- Political forecasting: Politicians and strategists use polls and other historical data to predict the likely outcome of elections. They may decide to shift their resources away from the areas where they’re likely to win or lose by a landslide, focusing instead on the races that are close.
- Social forecasting: It’s also important for politicians and strategists to understand changing beliefs, attitudes, and values so they can represent their constituents best (or at least understand how to appeal to them).
And That’s Just the Tip of the Iceberg…
Twenty use cases might be a lot to read (and write about), but this list barely scratches the surface – the use cases for time series analytics are everywhere. We’re sure you can already think of a few we haven’t mentioned.
If you’re interested in learning the practical skills to perform time series analytics, we can’t recommend our Time Series Analytics course enough. Taught by masters in the computer science and analytics fields, this four-week long course will arm you with the hands-on, cutting-edge skills and tools you need to excel at data analytics.
The course starts March 27. We’ll see you there!