Forecasting is the process of making predictions of the future based on past and present data and most commonly by analysis of trends. (Wikipedia)
A thousand years from now when someone writes the history of the human race, the emergence of Machine Learning will be hailed as a significant milestone. Machine Learning (ML), a branch of Artificial Intelligence (AI), enables computers to learn from data without being explicitly programmed. Today, ML has established itself as the key to unlocking the value from customer data. Netflix's movie recommendations, Facebook's ability to spot our faces, Google's self-driving cars are all early examples of ML-powered solutions. However, ML and AI are still in a nascent stage with the majority of industry leaders still struggling to cut through the hype and set right priorities for their businesses.
Maybe you are doing your demand forecasting completely wrong. To be more precise, there are two equally important outputs of demand forecasting and you may be focusing nearly all your energy on only one, and maybe even the wrong one. And the impact is that you may not be getting the forecast accuracy you want. Or even more important, that you may not be getting the service levels and inventory efficiencies that you need. And if that's true, you are not alone.
Well, I work in this area now, and since this is upvoted a bit I'll give my thoughts. And I'll assume you're constraining the term "demand forecasting" to how its often used in business contexts....as well as your your recent posts on issues getting RNN/LSTM to work your time-series data. IMO the best tool for most product/service demand prediction tasks is domain knowledge for good feature engineering and for getting your data to be more stationary. Why? Product/service demand forecasting problems often start with only few explanatory variables as well as those variables not explaining the variance well (more precisely, low mutual information) relative to the number of actual factors going into the demand. Contrast this with areas getting more media such as deep reinforcement learning, where states and actions are fully representable/observed (e.g., AlphaGo).
Another problem is that the more granular the forecast – SKU at store level by week, for example – the higher the forecast error tends to be. "For sure, the greater degree of error in the store-level forecast, the greater the impact on the lost sale calculation," Fenwick said. "However, even if we hit a 70% accuracy measure, we're still capturing 70% of the potential lost demand in the store due to stock outs. Which, from a forecasting perspective, is a lot better than capturing zero lost demand. As the saying goes, 'if you only forecast to sales, you'll only ever stock to … what you sold.'"
The National Oceanic and Atmospheric Administration is predicting an above-normal 2017 Hurricane Season, with five to nine hurricanes -- two to four of them Category 3 (winds at least 111 mph) or stronger. The weakness or absence of storm-suppressing El Niño climate conditions, combined with above-normal ocean surface temperatures and average or weaker vertical wind shear across the Caribbean and Atlantic Coast are factors pointing to an active hurricane season, said Ben Friedman, acting NOAA administrator. They expect that three named storms will make landfall in the U.S. April's Tropical Storm Arlene was a rare preseason storm, but it was also an indication of an active season ahead, Friedman said Thursday during a news conference at the NOAA Center for Weather and Climate Prediction in College Park, Md. In the 25 years since Category 5 Hurricane Andrew hit South Florida, forecasting accuracy has improved 65%, said Mary Erickson, deputy director at the National Weather Service.
It is the strongest storm since 2004, and Hurricane Matthew looks set to continue wreaking havoc on the southeast coast of the US. The storm hit Florida in the earlier hours of this morning, and forecasters say that it isn't done yet. They suggest that Hurricane Matthew could even loop back around on itself, hitting Florida for a second time - although forecasts are continually changing. Experts have explained to MailOnline the science behind Hurricane Matthew's strange behaviour. Gurricanes that hit the East Coast tend to move northwest.
WASHINGTON – With modern technology, people can watch hurricanes churn in real time and forecasts are on-target up to seven days in advance -- but experts say some puzzling storm traits are harder to solve. Using hurricane hunter aircraft, converted military drones, weather balloons and satellites that examine cyclones under various angles, "our observations are really telling us what is happening now," said Frank Marks, director of the Hurricane Research Division at the National Oceanic and Atmospheric Administration. "And also those observations are phenomenally useful to improving our ability to predict," he told AFP. All the collected data are immediately transmitted to meteorologists and entered into computer models that produce forecasts at the National Hurricane Center in Miami, Florida. Marks describes forecasts as simply "what we think might happen," saying experts' ability to make them has "improved dramatically for the last 35 years."
The SQL Server R Services available in SQL Server 2016 offer customers new opportunities to perform in-database advanced analytics. With SQL Server R Services, both open source R scripts and the high performance analytics algorithms in Microsoft R Server can be executed within SQL Server. Furthermore, you can continue to develop using familiar R Integrated Development Environments (IDEs) such as R Tools for Visual Studio, RStudio etc., and benefit from interactive development and debugging. The development process can be done on your local computer, while performing in-database computation without moving data in or out of the database. The produced R scripts are easy to operationalize on SQL Server.
My colleague Steve Liesman has published a report on the government's quarterly GDP report. Summed up, he found a large, persistent error in GDP between initial and final GDP reports. Not only is it off significantly, the government even gets the direction of growth wrong 30 percent of the time! Why is economic forecasting still so bad? Many feel that the tools being used to make the forecasts are simply inadequate.
We present an electricity demand forecasting algorithm based on Gaussian processes. By introducing a task-specific, custom covariance function k_power, which incorporates all available seasonal information as well as weather data, we are able to make accurate predictions of power consumption and renewable energy production. The hyper-parameters of the Gaussian process are optimized automatically using marginal likelihood maximization. There are no parameters to be specified by the user. We evaluate the prediction performance on simulated data and get superior results compared to a simple baseline method.