We’ve posted some great talks by Amelia Greenhall here on the blog and we’re excited to bring you another insightful presentation. Last year Amelia gave a wonderful talk about her weight loss journey and the power of using running averages. In this updated talk Amelia gives a more in-depth look about how using a 10-day moving average serves as an “early warning system” that puts helps put her back on the path of mindful eating. Filmed at the QS Silicon Valley meetup group
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This is similar to what The Hacker’s Diet recommends. (Which is also what Beeminder does.)
Yep! as I mentioned in the talk, I decided to do it after stumbling on the Hacker’s Diet.
Fascinating! I have been using a 14 day rolling average to track around 24 different items including weight, body fat percent, blood glucose levels & blood pressure.
One of the things I really liked here was Amelia’s comment that sometimes a line that doesn’t change is interesting and useful, too. What she means, in this case, I think, is that at the right scale and with the right transformation (in this case a simple moving average), what might have look like “noisy” or “random” or “static” data becomes coherent and meaningful.
Thanks, Gary! That’s exactly what I meant.
Very interesting discussion. While the use of any moving average period (10, 14) can be beneficial for a number of reasons, I personally believe the period of choice should have some significance in terms of ones cyclical behavior since the resulting calculation shows the typical value/trend for that period. This is why I use a 7-day simple moving average…so much of our lives are based around a weekly cycle and the resulting value tells me how my behavior has progressed over the last week. One could also argue that a monthly average is relevant in this regard, yet I’ve found that ~30-day averages introduce to much lag to be useful.
The Hacker’s Diet (where I got the idea from) makes a pretty conscious choice to use a 10 day average (see the Signal and Noise chapter in the section “Simple moving averages”, but also gets into exponentially smothing averages, which you could use to reduce the lag for a long period. I think the difference between 7 and 10 is mostly that 10 removes more of the jagged up and downs. Maybe I’ll recalculate my data later and see what the differences in the lines are.
Sorry, I missed the description of the reasoning behind the 10EMA, my 10 mo. old daughter must have pulled me away from my screen during that portion! A 10EMA is similar to a 7SMA in that it responds more to recent prices, making it less laggy that a 10SMA.
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