With increasing automation and expansion of Industry 4.0 and IoT, there’s also a growing demand for machine learning to optimize processes and improve outcomes. Machine learning is finding increased adoption, in part thanks to the huge amount of time series data collected by organizations all over the globe, and also due to the rise of big data management tools capable of conducting time series data analysis.
What is Time Series Data Analysis?
Time series data is collected over a period of time, usually but not always, at set intervals. It makes up a huge quantity of the data in our world and most of the data in IoT. It can be used to analyze past data to uncover patterns, then match those patterns with current or incoming data patterns. This highlights what variables could be creating changes in the data patterns.
Time series data analysis is extremely useful for forecasting in predictive analytics. However, one of the challenges with time series data—especially as related to IoT—is that there’s so much of it, and it’s usually coming in rapidly. More data is only useful if you have a way to understand what it’s telling you. Sightline EDM, a big data management tool, helps solve this issue by aggregating, analyzing, and providing insights. From visualizations that display the data in a quickly-digestible format, to being able to conduct time series data analysis in real-time as data comes in, big data management tools remove the issues of having too much data.
What is Time Series Data in Machine Learning?
Machine learning is a growing trend, and is likely to see even broader adoption in 2022, as more organizations understand what it can do and thus implement big data management tools capable of conferring the benefits. So, what’s time series data in machine learning? In many ways, time series data analysis is the foundation of machine learning. It feeds in the information that software solutions need to be able to apply machine learning and predictive analytics tools. This is an essential function in order to get future forecasts that are solid enough to use as a foundation for business decisions.
Machine learning can boost predictive analytics tools by creating more accurate forecasts. Predictive analytics works by combining historical information with real-time information to create a prediction of what is likely to happen in the future, based on similarity in data patterns or behavior. Machine learning automates the process of applying new data—both as it comes in and as you add it—to refine these predictions for increased accuracy.
This process is similar to the learning curve of humans. Starting a new job likely means a certain amount of training on the basic aspects needed to perform that job—just like starting with a set of data. Over time, that person will encounter unexpected scenarios and learn how to address them and, eventually, start to notice similar trends before an issue arises. The difference between machine learning and human learning is that machine learning can provide insights into massive amounts of data at a speed that humans can’t compete with. Over time, it is likely machine learning will be able to fill in gaps in information on its own.
The real value of modern big data analytics tools is that they allow organizations to maximize the benefits of both humans and machines by leveraging time series data analysis in machine learning. This will provide insights to your team so they can determine appropriate next steps.
Machine Learning Provides Advanced Data Insights
With an understanding of what time series data is in machine learning, it’s time to talk about the why of it. Machine learning is a tool for your team that can help them make better-informed decisions. It’s constantly adjusting likely outcomes based on what the new data is showing, and will ultimately help you achieve better efficiency, stronger ROI, and less downtime.
One of the most beneficial ways it can be used is in machine learning capacity management. Managing capacity is challenging because conditions are always changing. Supply chain can be impacted by demand spikes, machine maintenance, load limitations on technology infrastructures, and more. Because of this, organizations must frequently adjust forecasts. With machine learning capacity management, the most tedious, time consuming work is automated and tailored to the data of your specific organization. That means you can get new forecasts nearly simultaneously to new data coming in, and you never have to be surprised by a looming issue or miss out on an incoming opportunity. In addition, you can use machine learning in combination with AI to automate anomaly detection and prevent downtime.
Machine learning is in use in a huge number of industries, from manufacturing to IT. More industries are recognizing the value and adopting this tactic each year.
Sightline EDM’s Proven Time Series Data Analysis and Machine Learning Tools
Sightline Systems EDM software solution combines several big data management tools into an easy-to-use system, including machine learning capacity management. Sightline EDM can simplify the process of baselining and identifying resources or capacity availability. Then, it can provide information on how to either maintain those levels or adjust, as needed, to demand or usage fluctuations.
Because understanding what the data is telling you is vital to being able to act on it, Sightline EDM also provides custom forecast reports and alerts based on parameters or variables you choose that are unique to your organization. You can anticipate bottlenecks and when capacity will run out up to years in advance, and also be assured that those predictions are only improving over time.
Applying big data management tools to data collected over a period of time to detect historic and current patterns These patterns can provide insight into what is happening in your operations.
Machine learning relies on time series data analysis to provide the information it needs to create predictions and then adjust them based on more data.
Machine learning empowers your team to make better decisions and machine learning capacity management eliminates tedious, manual analysis tailors predictions to your unique data.