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.
How to prepare your retail and online production, inventory and distribution for changing COVID-19 measures?
External factors can have a huge impact on your demand forecast for specific products in specific locations and channels. The constantly changing government guidance during the current COVID pandemic can also cause huge swings in demand, especially when wide reaching regulation – such as decisions to close restaurants or limit movement – can come and go within hours for entire towns, cities or countries. To help businesses dynamically allocate their scarce resources of staff and inventory, the use of TIM and real-time instant ML can create forecasts that are as dynamic as the events that influence them. This can enable fast business decisions to take advantage of opportunities and limit the costs of reacting to current events.
Talk to our specialist about adaptive retail sales forecasting
The TIM Engine is perfectly suited for this application, because of the speed, resilience and ease of deployment. Other forecasting methods, such as statistical forecasting, are far too slow to react to the modern business climate. Single variate models will miss on the complex interaction between seasonal changes, externally driven changes to demand and externally driven changes to mobility. The speed of forecasting is also incredibly important in determining what factors are causing permanent changes to demand and customer behavior and which changes will be temporary.
Using the TIM Engine, an analyst can quickly iterate on models using dozens or even hundreds of features to get predicted impacts on demand in near real-time. This is a must-have tool for anyone in an organization who is responsible for planning of inventory and/or staffing levels.
As an example, using the TIM product we can immediately predict the impact a new restriction in a specific town would have on online ordering in specific postcodes. This can be used to ensure the correct allocation of capital equipment (trucks), staffing (drivers) and product (warehouses) even in advance of the restriction being implemented. With the TIM Engine, this forecast can be available in minutes to respond to significant events that may be happening within only a few days. We can see that a new announcement of a restriction to restaurants causes an immediate surge in online grocery demand that lasts for several days before subsiding to pre-restriction levels. This analysis can be extended to review the impact on specific products at a SKU level – including the ability to run independent demand forecasts for individual SKUs at individual stores in seconds.
Demand Data has real-time access to external data of significance, such as weather data (forecast and history) for any geographic point, as well as real-time COVID cases, mobility and restrictions for each postcode in most major countries. This data can instantly be prepared as inputs to be combined with sales data for specific products, channels or store locations. We have templates available which can plug into sales data at a store level and create the base models instantly (including COVID, weather and human mobility). From there, your analysts can iterate using other data or assumptions they have and get feedback in seconds on which assumptions or data are good predictors and which ones are not.
Talk to our specialist about adaptive retail sales forecasting
Watch a video taking you through this use case below:
The velocity of change in the mortgage industry is outpacing the abilities and reach of existing prepayment and profitability models, especially in the tail-risk coronavirus world. With over $16T total outstanding mortgages, the US mortgage market requires a scalable, accurate forecasting and modeling solution.
TIM enables users to iteratively forecast mortgage prepayment, delinquency, and default. This can help investors, GSE’s, servicers, lenders, and other stakeholders to evaluate and quantify the valuation and credit risk of their mortgage assets.
The US mortgage industry generates a vast quantity of data highly relevant to profitability and risk analysis. Some of this data is in users’ possession, some of it is publicly available, and some of it can be acquired. InstantML allows you to understand, differentiate, and quantify the relevance and impact of each data source to your forecasts.
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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