Electricity price forecasting competition

#artificialintelligence

The GEFCom competitions have been a great success in generating good research on forecasting methods for electricity demand, and in enabling a comprehensive comparative evaluation of various methods. But they have only considered price forecasting in a simplified setting. So I'm happy to see this challenge is being taken up as part of the European Energy Market Conference for 2016, to be held from 6-9 June at the University of Porto in Portugal.


Alphacat Report (September 15–30) – Alphacat – Medium

#artificialintelligence

As part of our efforts to be transparent and communicate regularly with our community, we are pleased to share this mid-month report, which includes our progress during these last two weeks and our outlook for the future. At the end of this month, one of the core parts of the Alphacat project -- the ACAT Store, was updated to V1.6.0. From these channels, users can directly view the latest industry news, official announcements, product introductions, etc. This section is expected to be open to users in the Store's next version. Users can check their recharge status, consumption status and remaining service times, at any time.


M⁴ Competition

@machinelearnbot

Forecasting accuracy is a critical factor for, among other things, reducing costs and providing better customer service. Yet the knowledge and experience available for improving such accuracy for specific situations is not always utilized. The consequence is actual and/or opportunity losses, sometimes of considerable magnitude. Empirical studies in the field of forecasting have compared the post-sample forecasting accuracy of various methods so that their performance can be determined in an objective, measurable manner. The M-Competitions are such empirical studies that have compared the performance of a large number of major time series methods using recognized experts who provide the forecasts for their method of expertise.


Demand Management Solutions Need To Be Flexible

Forbes - Tech

Demand management solutions have improved on many fronts. One area where some solutions have made particular progress is in the area of providing highly flexible solutions that can evolve as the company's business changes. Demand management solutions need to be flexible! Over time, companies often go on a journey where their forecast accuracy is improved by using more and more data and by forecasting at a more granular levels (For more on this topic, see this article which describes stages in demand forecasting maturity). To support this journey demand management solutions need to be flexible.


ARIMA And ETS Forecasting In R

#artificialintelligence

ARIMA And ETS Forecasting In RAccurately forecasting costs, sales, user growth, patient readmission, etc is an important step to providing directors actionable information. This can be difficult to model by hand or in Excel. In addition, using traditional methods like moving averages might not provide enough insight into the various trends and seasonality. Using models like the ARIMA and ETS provides analysts the ability to predict more accurately and robustly by considering multiple factors like seasonality and trend. What is even better is that languages like R and Python make it much easier for analysts and data teams to avoid all the work they would usually have to do by hand.