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.
Nowadays, trading is mostly automated, and when there is an order placed you bet it is likely a robot that hit the trigger. AI (robots) took over, it has been years since this trend started.
To build a profitable and sustainable trading system many elements are needed, from risk management, collection of the right data, back-testing etc. There is plethora of areas that can be solved with AI/ML tools, and they can be framed as problems for: forecasting, classification, or anomaly detection. All of them are problems that TIM can solve.
TIM is robust and fast. It can work with data sampled starting from milliseconds to years, data that contain gaps, irregularly sampled data (just like tick data are). It can also build new ML model in truly short time, even with each forecast (or classification).
Effort and time required to set up pipeline with TIM is reduced to fraction of what would be typically required. TIM, by design, automates most of the steps required for set up and operations, and offers robust ML solution capable to quickly adapt to structural changes.
Depending on the problem being solved, prediction horizon, and market, different data may be required. Knowing which data to use is typically part of well protected intellectual property.
If we take short term forecasting, having market (bars) data combined with technical indicators, correlated, or cointegrated assets would be a good start. In a game played with leveraged positions chasing tiniest deltas (movements) high quality data make difference.
TIM’s output consists of forecasted value per step (sample) over desired forecasting horizon.
The recent evolution of Internet of Things (IoT) technologies has resulted in the deployment of massive numbers of sensors in various fields, including manufacturing, energy and utilities, and logistics. These sensors produce huge amounts of time series data, but understanding the data generated and finding meaningful patterns remains an obstacle to successful IoT implementations.
A common problem that can be solved with IoT data is anomaly detection, where temporal patterns in the data are used to identify mechanical or electronic anomalies in a piece of equipment, prior to the occurrence of a failure. This approach can help to minimize downtime for manufacturing pipelines or other IoT networks, thus preventing potential blocks on revenue streams. It can also enable cost savings by allowing maintenance interventions to be scheduled only when necessary.
Machine learning techniques provide an ideal solution for solving anomaly detection problems. However, they are typically time-consuming and costly to implement. TIM provides a revolutionary solution to this problem by allowing the development of rigorous anomaly detection models with minimal lead time. This is due to its highly automated and exceptionally fast modeling algorithm.
Due to its speed, TIM’s anomaly detection can easily be applied at scale, to huge numbers of IoT instruments. In addition, the TIM algorithm is extremely lightweight, and can thus be run directly on edge devices, reducing the need for costly network communication.
Finally, TIM’s API-first infrastructure makes it simple to integrate models into a production workflow.
Anomaly detection can be performed on a single instrument output data field, or it can combine information from multiple fields. For example, the information from a number of manufacturing instruments might be used to predict a quality metric for a material being produced. Or multiple data points from a single instrument might be used to predict when failure is likely to occur.
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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