As illustrated across our website, time series are everywhere. Each industry vertical, each domain, each company comes into contact with time series in one way or another. In the use case library below, you can explore the extensive applications of time series, or look for applications in your specific field of interest.
All efforts in marketing and advertising today rely on a wide array of data sources, usually including both internal and external datasets. While this allows for deep insights and highly efficient marketing campaigns, it can also cause problems.
Imagine you are analyzing an ad campaign, when you realize the number of impressions being delivered per day dropped dramatically at a certain date, two weeks ago. A frantic investigation reveals that something has changed in the external data source being used to target potential customers, but the vendor had not alerted you to this. This could equally affect a customer’s insights or segmentation project.
Using machine learning techniques for anomaly detection, you could have detected this ahead of time, instead of discovering the problem weeks or months down the line. However, implementing such a system from scratch requires much time and specialized expertise.
TIM provides a much-needed new approach to this problem, by making it possible to implement robust anomaly detection routines with minimal lead time. Firstly, it is highly automated, meaning no data science experience is required to build effective models. Secondly, the model training process is stunningly fast, taking only a few seconds for a typical dataset – this makes it very easy to build effective models and also allows for huge scaling possibilities. Finally, the API-first infrastructure makes it simple to integrate models into a production workflow.
TIM’s anomaly detection capabilities rest upon first defining “normal behavior” for a given variable or data field (achieved using the TIM forecasting model) and then extending that with an “anomalous behavior” learning algorithm.
Ultimately, an anomaly detection platform with TIM at the center can provide organizations with much-needed confidence in the data that is fundamental to their operation.
TIM’s anomaly detection capabilities can be exploited using a single input variable – for example, if you want to detect anomalies for 1,000 fields from an external data source, you could build one model for each field. It can also be achieved when using multiple input variables. For example, you might want to detect anomalies in conversions, using inputs such as numbers of impressions across multiple marketing channels, economic metrics and more. TIM can handle both types of anomaly detection problems smoothly.
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