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.
Every year billions of transactions are made by payment cards worldwide. Card companies spend vast amounts of resources to keep card operations fast and secure. Fraudulent activity related to misuse of cards can relate to both debit and credit cards. Costs incurred due to credit cards fraud can go as high as tens of billions of dollars annually.
This is a broad topic, securing card operations does not stop only at protecting data trying to avoid data breaches. Card issuers, banks and merchants need to take countermeasures to combat card payment fraud. Considering vast volumes and velocity, it would not be possible without automation, and AI/ML comes as natural choice.
TIM’s RTInstantML technology builds ML models in automated fashion in fraction of time. Its capabilities cover use cases for time series forecasting, classification and anomaly detection. Detecting fraudulent activity is a task for classification and/or anomaly detection.
Due to its hyper automation and speed, (re)building new ML model every hour, couple of minutes, or on demand for specific transactions is fully possible.
Yet, from operations perspective, TIM can be deployed rapidly fast, and is easy to operate. It can run in cloud or on the edge, scales automatically, is robust enough to withstand defects in data and comes with support of various sampling rates.
In classification cases for detection of fraudulent activity, it is necessary to provide labelled data, i.e., to include flag indicating to which class given activity belongs to (1 for fraudulent, or 0 otherwise).
Explanatory variables would typically include: amount, geo location information, time parameters, effective credit limit, descriptors of previous transaction, channel etc. In general, there are additional predictors used by banks/card companies that (improve accuracy and) are rather kept undisclosed to not give any hints to fraudsters.
TIM’s output in classification tasks is a value ranging from 0 to 1, closer to 1, the bigger probability activity is fraudulent.
Michal Bezak - Tangent Works
Solution Template (Classification) Solution Template (Forecasting) Solution Template - Condition monitoring Solution Template - Water pump
Companies across a variety of industries rely on machines: pumps, engines, elevators, turbines, etc. Some are more complex than others, but they surely have one thing in common – degradation of material. With each cycle (moment) of operation, components are losing their original physical parameters. Regular checks, diagnostics, and maintenance, or even replacement is an important part of machine operations.
The ideal scenario is to avoid failure of a given machine, thus being pro-active rather then reactive is for many businesses the only option. Also, acting at the right time has real financial implications. Imagine two extreme situations:
Predictive maintenance solutions can provide the optimal time for maintenance. Thanks to the data coming from sensors and AI/ML, it is possible to get advice, almost in real-time, on what is the best time to take action.
TIM can build automated ML models from time series data and predict the time remaining (Remaining Useful Life, RUL) or classify whether the device is already in a window (zone) of possible failure within a certain period of time (cycles).
Data from machine sensors are often sampled in seconds, or even milliseconds. TIM can work with data sampled in any sampling rate starting from milliseconds.
Also, effort and time required to set up TIM for production use is reduced to a fraction of what would be typically required. TIM, by design, automates most of the steps required for set-up and operations, and offers a robust ML solution.
Input: explanatory variables should include measurements from relevant sensors, values of key settings, information about failures, cycle numbers and/or other.
Output: TIM’s output consists of forecasted RUL value or binary classification (1 or 0), depending on given scenario.
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.
