An insurance company struggled with the ability to identify areas overexposed in terms of total insured value overall or per given peril
Creation of an application to present a heatmap of insured values (total / per peril) for all area granularity levels allowing to easily spot overexposed areas as well as many filtering options and statistics of filtered items with the possibility to analyse it further.
Client can identify overexposed areas and coordinate with underwriters to stop offering insurance for items in those areas to prevent potential loss in case natural catastrophe happens.
Darts – Time Series forecasting
open source by Unit8
Any quantity varying over time can be represented as a time series: sales numbers, rainfall, stock prices, CO2 emissions, Internet clicks, network traffic, etc. Time series forecasting — the ability to predict the future evolution of a time series— is a key capability in many domains where anticipation is important. Although there exist many models and tools for time series, they are often non-trivial to work with, because they each have their own intricacies and cannot always be used in the same way
Darts is our open source library for time series forecasting, attempting to simplify time series processing and forecasting in Python
Speed-up the process related to time series forecasting in order to
– Decrease costs
– Improve accuracy
– Reduce manual work