Did you know there were principles of forecasting? I don't mean like the positions of the planets. Which for time spans of tens of thousands of years is fairly mechanical. The kind of forecasting I'm talking about involves events that are less deterministic than the motions of the planets. And yet there are principles.

The first is to classify the methodology. Are you starting with numbers or guesses? Which is to say how good is your data base? If you have numbers, what kind of precision is attached? Do you use the numbers directly? Or do you use statistical methods to tease out "useful" information?

OK. You have some data. Now you have to select a method of analysis that is both suitable to the data and the purpose for which it will be used. Is this an investment decision? Or just a report on something to keep an eye on? Do you have a business plan in hand or just a casual "this seems like a good idea"?

The above pages are full of annotated charts with little pop-up explanation boxes to help you understand the charts.

And if that isn't enough the authors of these pages and the accompanying book will give you free help if you describe your problem(s) to them.

We have come a ways and surely it can't be just to talk about forecasting methods. Well yes and no. I want to talk about climate. Climate forecasting.

J. Scott Armstrong, of the Wharton School, University of Pennsylvania, and Kesten C. Green, of the Business and Economic Forecasting Unit, Monash University have done a short audit of IPCC climate science [pdf] based on the forecasting principles outlined above.

I think it would be good to start with the title which really gets to the heart of the matter.

Global Warming: Forecasts by Scientists versus Scientific Forecasts

In 2007, a panel of experts established by the World Meteorological Organization and the United Nations Environment Programme issued its updated, Fourth Assessment Report, forecasts. The Intergovernmental Panel on Climate Change's Working Group One Report predicts dramatic and harmful increases in average world temperatures over the next 92 years. We asked, are these forecasts a good basis for developing public policy? Our answer is "no".

Much research on forecasting has shown that experts' predictions are not useful. Rather, policies should be based on forecasts from scientific forecasting methods. We assessed the extent to which long-term forecasts of global average temperatures have been derived using evidence-based forecasting methods. We asked scientists and others involved in forecasting climate change to tell us which scientific articles presented the most credible forecasts. Most of the responses we received (30 out of 51) listed the IPCC Report as the best source. Given that the Report was commissioned at an enormous cost in order to provide policy recommendations to governments, the response should be reassuring. It is not. The forecasts in the Report were not the outcome of scientific procedures. In effect, they present the opinions of scientists transformed by mathematics and obscured by complex writing. We found no references to the primary sources of information on forecasting despite the fact these are easily available in books, articles, and websites. We conducted an audit of Chapter 8 of the IPCC's WG1 Report. We found enough information to make judgments on 89 out of the total of 140 principles. We found that the forecasting procedures that were used violated 72 principles. Many of the violations were, by themselves, critical. We have been unable to identify any scientific forecasts to support global warming. Claims that the Earth will get warmer have no more credence than saying that it will get colder.

• Agreement among experts is weakly related to accuracy. This is especially true when the experts communicate with one another and when they work together to solve problems. (As is the case with the IPCC process).

• Complex models (those involving nonlinearities and interactions) harm accuracy because their errors multiply. That is, they tend to magnify one another. Ascher (1978), refers to the Club of Rome's 1972 forecasts where, unaware of the research on forecasting, the developers proudly proclaimed, "in our model about 100,000 relationships are stored in the computer." (The first author was aghast not only at the poor methodology in that study, but also at how easy it was to mislead both politicians and the public.) Complex models are also less accurate because they tend to fit randomness, thereby also providing misleading conclusions about prediction intervals. Finally, there are more opportunities for errors to creep into complex models and the errors are difficult to find. Craig, Gadgil, and Koomey (2002) came to similar conclusions in their review of long-term energy forecasts for the US made between 1950 and 1980.

• Given even modest uncertainty, prediction intervals are enormous. For example, prediction intervals expand rapidly as time horizons increase so that one is faced with enormous intervals even when trying to forecast a straightforward thing such as automobile sales for General Motors over the next five years.

In electronics we can work with very complex systems because the interactions are strictly limited. How is this done? A marvelous Bell Labs invention called the transistor. It isolates as well as performing other useful functions.

The electronics guys, with lots of knowledge and isolation plus simple models, are real happy when their predictions of what will happen next in a circuit comes within 5%. The climate guys say they can tell within better that 1%. What are the odds?

When you have lots of things or some very complex things interacting, prediction gets hard. As a very great Yogi is reputed to have said: "Prediction is very difficult, especially about the future."

Cross Posted at Power and Control