Unevenly spaced time series analysis - oroup.com

A number of years ago I bought a Withings connected scale. It’s great. You get on every morning, it logs your weight and uploads it to the internet where it can be analyzed.

Recently, I decided to use the Withing API to build a plot that I could include on my homepage to keep an eye on my weight. Something every weight-watcher knows though is your weight fluctuates and so it’s great to keep an average. Unfortunately, unevenly spaced time series do not interact well with simple moving averages, so I found this paper and converted the code into javascript:

var tau = 1000 * 60 * 60 * 24 * 3.5 // 1/2 week
var i = 0
var left = 0
var t_left_new, roll_area, left_area, width, weight, y2
var avg_data = [[json_data[0][0], json_data[0][1]]]
roll_area = left_area = json_data[0][1] * tau
for (i = 1; i < json_data.length; i++) {
  roll_area += (json_data[i][1] + json_data[i – 1][1]) / 2 * (json_data[i][0] – json_data[i – 1][0])
  roll_area = roll_area – left_area
  t_left_new = json_data[i][0] – tau
  while (json_data[left][0] < t_left_new) {
    roll_area = roll_area – (json_data[left][1] + json_data[left + 1][1]) / 2 * (json_data[left + 1][0] – json_data[left][0])
    left++
  }
  width = json_data[left][0] – t_left_new
  if ((left === 0) || (width === 0)) {
    left_area = width * json_data[0][1]
  } else {
    weight = width / (json_data[left][0] – json_data[left – 1][0])
    y2 = json_data[left – 1][1] * weight + json_data[left][1] * (1 – weight)
    left_area = width * (y2 + json_data[left][1]) / 2
  }
  roll_area = roll_area + left_area
  avg_data[i] = [json_data[i][0], roll_area / tau]
}

var tau = 1000 * 60 * 60 * 24 * 3.5 // 1/2 week

var i = 0

var left = 0

var t_left_new, roll_area, left_area, width, weight, y2

var avg_data = [[json_data[0][0], json_data[0][1]]]

roll_area = left_area = json_data[0][1] * tau

for (i = 1; i < json_data.length; i++) {

roll_area += (json_data[i][1] + json_data[i – 1][1]) / 2 * (json_data[i][0] – json_data[i – 1][0])

roll_area = roll_area – left_area

t_left_new = json_data[i][0] – tau

while (json_data[left][0] < t_left_new) {

roll_area = roll_area – (json_data[left][1] + json_data[left + 1][1]) / 2 * (json_data[left + 1][0] – json_data[left][0])

left++

}

width = json_data[left][0] – t_left_new

if ((left === 0) || (width === 0)) {

left_area = width * json_data[0][1]

} else {

weight = width / (json_data[left][0] – json_data[left – 1][0])

y2 = json_data[left – 1][1] * weight + json_data[left][1] * (1 – weight)

left_area = width * (y2 + json_data[left][1]) / 2

}

roll_area = roll_area + left_area

avg_data[i] = [json_data[i][0], roll_area / tau]

}

The algorithm expects data in a variable called json_data. It’s an array of arrays, each of which is a timestamp and data pair. (This is the form that Highcharts expects.) The key factor is tau, the length of the time window over which you’re averaging. I have it set to a week here which I think is about right for looking at human weight. (3.5 days works quite well too.)

The results are here:

The code in context and a cleaner running example are both available. You can also see the raw data I use to fuel the graph.