All organisations want to understand what has happened in the past and what will happen in the future.\u00a0 The use of statistics and statistical thinking is essential to be a better forecaster but that doesn’t mean it is easy to do!\u00a0 At the same time, we are bombarded with forecasts in the media and that can make it difficult to decide which forecasts to pay attention to and which can be ignored.<\/p>\n
My course “Identifying Trends & Making Forecasts<\/a>” is all about doing the basics right when it comes to analysing trends and making predictions.\u00a0 To support this course, this post makes available a variety of material in the public domain covering the following themes:-<\/p>\n <\/p>\n For more details, please read the relevant section below.<\/p>\n <\/p>\n If you are attending my “Identifying Trends & Making Forecasts<\/a>” course, then you should read the 1st post as many of themes explored in that will be discussed in the course.<\/p>\n <\/p>\n I am passionate about forecasters tracking their performance, measuring their skill and identifying ways to improve.\u00a0 The simplest way to track and measure forecasting skill is to compare your forecasts with a simple baseline (or dumb) forecast.\u00a0 A baseline forecast is one that requires no skill such as “same as last time” or “equal to long term average”, etc.\u00a0 Even then, it is possible to make mistakes in choosing a suitable baseline.<\/p>\n If you are attending my “Identifying Trends & Making Forecasts<\/a>” course, then you should attempt the survey linked in the 1st post about Donald Trump as there will be a discussion during the course on what this tells you about baselines.<\/p>\n <\/p>\n If you are attending my “Identifying Trends & Making Forecasts<\/a>” course, then you will be asked to critique the BBC article in the 1st link since you will be analysing this data in one of the case studies!<\/p>\n There is one article I will link here but not put in the list above since it is not based on statistical thinking!\u00a0 Instead, it is based on what is known as chartism which is nothing more than superstition & astrology.\u00a0 I was unfortunate to come across it in the 1990s but when I looked at long term trends in voting intentions in the UK since the 1960s<\/a>, I could not get over how they could be explained with chartist concepts.<\/p>\n <\/p>\n Here are some examples of prediction models I have built & published in the public domain.\u00a0 The 3rd link is an example of how to evaluate the success or failure of a forecasting model and will be discussed in my course “Identifying Trends & Making Forecasts<\/a>“.<\/p>\n <\/p>\n Here are two posts on measuring forecasting skill.<\/p>\n <\/p>\n A well known quote from George Box is “all models are wrong but some are more useful than others<\/em><\/span>.”\u00a0 The same can be said of forecasts “all forecasts are wrong but some are more useful than others.<\/em><\/span>”\u00a0 To my mind, this statement will only be true if you undertake post-mortems of your forecasts and seek to learn lessons.\u00a0 Here are some examples of post-mortems.<\/p>\n <\/p>\n Any forecaster worth their salt should be publishing their track record in a format that is:-<\/p>\n My own track record for forecasting elections can be found here<\/a>.\u00a0 It is a public document and I consider it to be transparent and traceable for the most part since I link to all relevant materials.\u00a0 It is trackable in the sense that my forecasts and errors are in one place but I should add a few features to make it easier to extract this information.<\/p>\n It is actually very hard to find other forecasters who do this but here are two examples I have found<\/p>\n <\/p>\n The following 4 books underpin a lot of what I teach in my forecasting course.\u00a0 What’s great about all of them is that they take different angles on the forecasting conundrum but together they make a great collection.<\/p>\n <\/p>\n If you would like to book a training course in Forecasting, then please contact me<\/a>.<\/p>\n\n
\nA. What makes a good forecaster?<\/strong><\/span><\/h4>\n
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\nB. How to identify a suitable baseline forecast<\/span><\/strong><\/h4>\n
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\nC. Some real-life identification of trends<\/strong><\/span><\/h4>\n
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\nD. Some real-life forecasting models<\/span><\/strong><\/h4>\n
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\nE. How to identify & measure forecasting skill<\/strong><\/span><\/h4>\n
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\nF. Learning the lessons especially when your forecasts are wrong<\/strong><\/span><\/h4>\n
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\nG. Publish your track record<\/strong><\/span><\/h4>\n
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\nH. Recommended books about forecasting<\/strong><\/span><\/h4>\n
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