Time Series Data: What is It, And How Can You Collect It?
Every day, everywhere, professionals use data to make informed predictions. Meteorologists use past weather data to predict future weather. Stockbrokers bet on their data, placing huge amounts of money on the line when investing. Doctors measure electrical currents to monitor a patient’s heart health. If any of them make the wrong decisions, the consequences could be dire. But how do the people in these examples use their data to give them faith in their choices?
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They rely on time series data.
Read on to find out precisely what this type of data is and see more examples of time series data in the real world. You’ll also learn why you might want to use time series data in your business and how you can efficiently collect it.
What is Time Series Data?
Let’s establish what we’re talking about when we say “time series data.” Sometimes known as “time-stamped data,” time series data is a collection of data points on a single subject that attaches a time period to each value. This data is indexed in chronological order to create a sequence that can be observed for patterns over time.
The value you use could be more or less anything you can measure, although it usually is extracted from a consistent source. The data depends on time in some way so that a specific time can be tied to it, which leaves a vast scope of options.
What is a time series graph?
People usually gather time series data to track change over time. Although there isn’t a limit on how long you can record time series data, people typically measure it over a fixed time interval. Therefore, a graph of data points is often the best way to look at the results.
Time series graphs plot the variable you’re measuring on one axis (usually Y) against time parameters on the other axis (usually X). In most cases, data points are plotted and connected with straight lines. This allows you to see trends or patterns more easily.
Here are some valuable examples of time series data graphs.
3 Time Series Data Examples
Time series data can be found everywhere since everything we observe has the characteristic of being bound by time. This data is essential for the functioning of modern life.
Systems constantly produce a relentless stream of time series data. It can then be used in numerous ways across nearly every industry. What are some real-world examples of time series data being used?
Time series data in weather forecasting
Meteorologists are given the difficult task of predicting the weather days in advance. To create consistent forecasts that are even remotely accurate, they analyze time series data from the past. Regularly recorded climate data and models can help track, monitor, and anticipate future weather events.
Time series data help meteorologists forecast more than just rises and falls in temperature. They also use rainfall measurements, sunspots, migratory wildlife patterns, electrical grid strain, and global shipping routes.
Time series data in healthcare
Healthcare professionals use time series data in a whole range of medical technologies. One example of time series data is produced by the electrocardiogram (ECG), more commonly known as a heart monitor. By measuring electrical currents in the heart, this device records how often and regularly the heart beats over intervals of time.
Medical technology can record data points over a linear graph. But rather than predicting future events, doctors and patients can identify anomalous, sometimes life-threatening readings.
Time series data in finance
Financial institutions regularly rely on time series metrics to gain at least some certainty about the market. You know that squiggly line on a stock market chart? It tracks the movement of time series data points, recorded over regular intervals.
Financial data can be tracked over the short term (the price of a currency every 30 minutes during the day) or long-term (a stock’s price at the close of each business week over five years). Analyzing time series data also helps investors predict the stability of interest rates, financial portfolios, and other market conditions.
The Two Types of Time Series Data
So, time series data is collected observations measured over time. These specific time intervals will be calculated in one of two ways.
Metric time series data
Metric data is measurement data gathered at regular time intervals. For example, you can monitor the weather temperature every second of the day and collect a set of data from that. The set can be modeled or used for forecasting because you can fit what happened in the past into probable patterns for the future.
Health monitoring and system cluster monitoring are examples of metric time series data.
Event time series data
Event data refers to measurements gathered at irregular time intervals. An example is log data between software applications and operating systems. It can’t be easily modeled or forecasted, as you can’t predict exactly when individual events will happen again.
Logs and application traces are examples of event time series data.
The Analysis of Time Series Data
You’ve read about some of the ways time series data is crucial to the functioning of certain industries. In these instances, if the data wasn’t ordered chronologically, making sense of its insights would be nearly impossible. In one time series data example, forecasting the weather, a meteorologist recording the highest and lowest temperatures for the last five days is useless unless they know which temperatures correspond to each day. This is why sometimes only time series data will do the job.
But how is time series data analyzed to produce meaningful insights?
Pattern identification
People commonly conduct time series data analysis to identify patterns. If a pattern tends to repeat itself, this is known as autocorrelation. Data patterns can also be analyzed to determine upticks or downturns, allowing individuals to act fast to reinforce or change their strategy.
Forecast of future trends
If patterns repeat themselves at regular intervals, this is known as seasonality. And, while no one can predict the future, time series data that displays seasonality allows companies to forecast future trends by identifying this past behavior and extending it into the future.
Finding opportunities to clean data
Data points outside a set of data’s normal range can skew results, and you want to clearly see when these exist. The way time series data is presented allows you to quickly identify outliers so you can either remove them or investigate why they’ve occurred.
Identifying stationarity
Stationarity is a measure of how small the changes in a time series variance are over time. A stable currency could be measured over five years and present similar data points with low variance. You would be able to say this time series data has high stationarity.
How Can You Gather Time Series Data?
Before analyzing it, you need to get your initial time series data set into one location. Web scraping is the process of extracting data from a website or webpage, usually into a spreadsheet or local file on your PC. It’s one of the most efficient ways to collect data from the internet, useful in pretty much any situation where data needs to be moved from one place to another.
A lot of websites automatically block web scraping. But there is a solution to prevent this. An API is a programming code that enables data exchange between two software products. In the case of web scraping, the API requests access to specific data (for example, the prices of a product from price comparison sites over the last month). A web scraping API communicates with the price comparison websites’ API in a single call while specifying how they must provide the data back.
For a more in-depth look, head over to our tutorial on how web scraping works.
What are some of the benefits of a web scraping API?
- Saves energy and time — manually scraping the web for data is incredibly time-consuming, whereas an application can run faster and often in the background
- Easy to set up — a web scraping API will collect data from the whole domain, giving you a wealth of relevant data through just one process.
- Accurate data — an API removes the element of human error when manually scraping and avoids small data errors that can become more significant problems.
- Multiple data delivery — a web scraping API can deliver data in various formats, like XML, CSV, and JSON; sending it to online cloud services like Dropbox or Google Cloud
To learn more about the broader picture of data collection, check out our blog post on building a complete data collection strategy.
Final Thoughts
Now you’ve got a full rundown of time series data; the next step is collecting your own data set.
Try Scraping Robot for free and get 5000 free scrapes to get started. There’s no commitment and you can explore what analyzing time series data could do for your business. We’ll see you on the other side!
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