Validating fuzzy logic values

06-Feb-2015 16:48

In the same way, you shouldn't consider a forecast experiment to be complete until you find out whether the forecast was successful.

The three most important reasons to verify forecasts are: There are many types of forecasts, each of which calls for slightly different methods of verification.

The table below lists one way of distinguishing forecasts, along with verification methods that are appropriate for that type of forecast.

David Stephenson has proposed a classification scheme for forecasts. Murphy, 1986: The attributes diagram: A geometrical framework for assessing the quality of probability forecasts.

The verification can be qualitative ("does it look right? A forecast is like an experiment -- given a set of conditions, you make a hypothesis that a certain outcome will occur. Jewson, S., 2003: Use of the likelihood for measuring the skill of probabilistic forecasts.

You wouldn't consider an experiment to be complete until you determined its outcome. Schwierz, 2008: Quantile-based short-range QPF evaluation over Switzerland.

It is often possible to convert from one type of forecast to another simply by rearranging, categorizing, or thresholding the data.

The examples are all drawn from the meteorological world (since the people creating this web site are themselves meteorologists or work with meteorologists), but the verification methods can easily be applied in other fields.

If we take the term forecast to mean a prediction of the future state (of the weather, stock market prices, or whatever), then forecast verification is the process of assessing the quality of a forecast. Ziehmann, 2003: A note on the use of the word 'likelihood' in statistics and meteorology.

The forecast is compared, or verified, against a corresponding observation of what actually occurred, or some good estimate of the true outcome. In either case it should give you information about the nature of the forecast errors.

They are appropriate for verifying estimates as well as forecasts.

Does not provide source code (sorry, but what language would we use? However, the simple methods are relatively easy to code, and the complex ones give references to people who have developed them or are working on them. Ziehmann, 2003: Five guidelines for the evaluation of site-specific medium range probabilistic temperature forecasts. T., 2008: The impenetrable hedge: a note on propriety, equatability, and consistency.

validating fuzzy logic values-13

speed dating auckland north shore

The methods range from simple traditional statistics and scores, to methods for more detailed diagnostic and scientific verification. stratifying results Methods: Standard verification methods: Methods for dichotomous (yes/no) forecasts Methods for multi-category forecasts Methods for forecasts of continuous variables Methods for probabilistic forecasts Scientific or diagnostic verification methods: Methods for spatial forecasts Methods for probabilistic forecasts, including ensemble prediction systems Methods for rare events Other methods Sample forecast datasets: Finley tornado forecasts Probability of precipitation forecasts Some Frequently Asked Questions Discussion group References: Links to other verification sites References and further reading Contributors to this site Verification and art Describes methods for forecast verification, including their characteristics, pros and cons. (2008): Proper Scores for Probability Forecasts Can Never Be Equitable., Monthly Weather Review, 136, pp 1505-1510.