Forecasting Methods: Detail without Informed Guidance
February 27, 2017
Let’s create a scenario. You are a person trying to figure out how to index a chunk of content. You are working with cancer information sucked down from PubMed or a similar source. You run an extraction process and push the text through an indexing system. You use a system like Leximancer and look at the results. Hmmm.
Next you take a corpus of blog posts dealing with medical information. You suck down the content and run it through your extractor, your indexing system, and your Leximancer set up. You look at the results. Hmmm.
How do you figure out what terms are going to be important for your next batch of mixed content?
You might navigate to “Selecting Forecasting Methods in Data Science.” The write up does a good job of outlining some of the numerical recipes taught in university courses and discussed in textbooks. For example, you can get an overview in this nifty graphic:
And you can review outputs from the different methods identified like this:
Useful.
What’s missing? For the person floundering away like one government agency’s employee at which I worked years ago, you pick the trend line you want. Then you try to plug in the numbers and generate some useful data. If that is too tough, you hire your friendly GSA schedule consultant to do the work for you. Yep, that’s how I ended up looking at:
- Manually selected data
- Lousy controls
- Outputs from different systems
- Misindexed text
- Entities which were not really entities
- A confused government employee.
Here’s the takeaway. Just because software is available to output stuff in a log file and Excel makes it easy to wrangle most of the data into rows and columns, none of the information may be useful, valid, or even in the same ball game.
When one then applies without understanding different forecasting methods, we have an example of how an individual can create a pretty exciting data analysis.
Descriptions of algorithms do not correlate with high value outputs. Data quality, sampling, understanding why curves are “different”, and other annoying details don’t fit into some busy work lives.
Stephen E Arnold, February 27, 2017
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Forecasting Methods: Detail without Informed Guidance : Stephen E. Arnold @ Beyond Search