Time-series – as the name suggests – is recording points of data against time. This time can be as large as years or as precise as microseconds or nanoseconds. Using this data structure we can apply time-series analysis and time-series forecasting to it.
Analysis looks at how some metric changes over time and what trends or patterns emerge that we couldn’t see just by looking at the data – think back to GCSE maths when you looked at distance-time graphs to model the story of the tortoise and the hare. You were able to understand the movements of both the animals – this is time-analysis. Simple, yes, but time-analysis none the less.
Forecasting looks at using past data to predict a future times’ metric values. Taking that example of the tortoise and the hare, let’s just consider the hare. What if the distance to be covered was twice that of the original story, what time would we expect the hare to finish? Using time-series forecasting we could predict this.
It’s also worth mentioning the underlying data structure that enables this, time-series databases. These databases are designed to handle timestamp data with extreme precision, compress and auto aggregate data and perform optimised querying over large time ranges of time-series data.
All that being said, the best and most common uses of time-series analysis and forecasting is in FinTech, control charts and anomaly detection.
So, where can Kainos or the Applied Innovation team apply this technology? IoT device networks and other sensory technologies provide enormous data lakes of data.
Leveraging this continuous stream of data and feeding that into scalable and capable time-series data structures we can start to find insights that weren’t just difficult to spot at first, but impossible. Insights into people movement from on-street CCTV cameras or Wi-Fi connectivity can allow us to make better decisions for things such as footpath repairs or where to place items in a clothes store for most traction. Strange or unexpected actions could be flagged for source control commits or server performance using anomaly detection.
If this interests you then email the Applied Innovation at email@example.com.