Remove 2017 Remove Forecasting Remove Risk
article thumbnail

Chart Snapshot: Fan Charts

The Data Visualisation Catalogue

A Fan Chart is a visualisation tool used in time series analysis to display forecasts and associated uncertainties. Also, as the forecast extends further into the future, uncertainty grows, causing the shaded areas to widen and give this chart its distinctive ‘fan’ appearance.

article thumbnail

Infor’s Velocity Summit Highlights Multiple Advances and Enhancements

David Menninger's Analyst Perspectives

Infor introduced its original AI and machine learning capabilities in 2017 in the form of Coleman, which uses its Infor AI/ML platform built on Amazon’s SageMaker to create predictive and prescriptive analytics. An innate conservatism, aversion to risk and the need to ensure complete accuracy are the human factors at work in this delay.

Finance 130
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

TBM helps CIOs translate tech spending to business outcomes

CIO Business Intelligence

Cost transparency and accurate budget forecasting are two major parts of the TBM framework, Guarini says. The US Office of Management and Budget has also pushed agencies to use TBM practices since 2017. Energy use has become an important expense to monitor as well, along with more traditional IT costs and risk management.

ROI 100
article thumbnail

Can Predictive Analytics Help Traders Navigate Bitcoin’s Volatility?

Smart Data Collective

The evidence demonstrating the effectiveness of predictive analytics for forecasting prices of these securities has been relatively mixed. Many experts are using predictive analytics technology to forecast the future value of bitcoin. The good news is that predictive analytics technology can reduce risk exposure for these investors.

article thumbnail

Top 10 Analytics And Business Intelligence Trends For 2020

datapine

This is one of the major trends chosen by Gartner in their 2020 Strategic Technology Trends report , combining AI with autonomous things and hyperautomation, and concentrating on the level of security in which AI risks of developing vulnerable points of attacks. It’s an extension of data mining which refers only to past data.

article thumbnail

The trinity of errors in financial models: An introductory analysis using TensorFlow Probability

O'Reilly on Data

Errors in analysis and forecasting may arise from any of the following modeling issues: using an inappropriate functional form, inputting inaccurate parameters, or failing to adapt to structural changes in the market. Time-variant distributions for asset values and risks are the rule, not the exception.

Modeling 198
article thumbnail

Our quest for robust time series forecasting at scale

The Unofficial Google Data Science Blog

by ERIC TASSONE, FARZAN ROHANI We were part of a team of data scientists in Search Infrastructure at Google that took on the task of developing robust and automatic large-scale time series forecasting for our organization. So it should come as no surprise that Google has compiled and forecast time series for a long time.