{
  "id": 20153241,
  "title": "Turning noisy bathroom-scale data into a projected date: SMA vs. EMA vs. linear regression",
  "url": "https://urgent.news/2026/11/15/turning-noisy-bathroom-scale-data-into-a-projected-date-sma-vs-ema-vs",
  "topic": "tech",
  "section": "Tech",
  "published": "2026-11-15T09:52:52.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/josefwebdeveloper/turning-noisy-bathroom-scale-data-into-a-projected-date-sma-vs-ema-vs-linear-regression-4bdi"
  },
  "original_language": "en",
  "account": "Three common methods were tested to predict weight loss progress from daily bathroom scale readings: simple moving average (SMA), exponentially smoothed moving average (EMA), and linear regression. Each approach has its own strengths and weaknesses when it comes to forecasting when a target weight can be reached.\n\nThe SMA method calculates the average weight over the past seven days. This gives a straightforward average that falls about three days behind the current weight. However, every point in the window is given equal weight, so older data drops off quickly and the average is not very responsive to recent changes.\n\nThe EMA applies a smoothing factor of 10% to each day's weight, gradually adjusting the trend. This allows the average to move more smoothly and reduces the impact of a single outlier. However, with a smoothing factor of 0.1, the EMA lags behind the actual weight loss and responds slowly to steady progress. It's better for checking if one is still on track, but less useful for pinpointing a specific date.\n\nLinear regression fitted a straight line through the last few data points to estimate the daily weight change. By extending this line to the goal weight, a date when the target weight will be reached can be calculated. The SMA and EMA methods don't provide a direct rate of change, while regression does. However, regression assumes a constant rate of change, which may not hold true as weight loss progresses and becomes slower. Outliers can also skew the regression line more than the other methods.",
  "summary": "Body weight is a terrible time series. On a single morning it can swing by a kilogram or more because of water, salt, glycogen and whatever is still in your gut. Yet the question everyone actually asks is a forecasting question: \"At this rate, when do I hit my goal?\" I built a free goal-weight date calculator ( Weight Forecast , part of Life Forecast) and had to pick how to turn noisy weigh-ins…",
  "key_points": [
    "Simple moving average (SMA) calculates 7-day average weight, lagging about 3 days",
    "Exponentially smoothed moving average (EMA) uses 10% smoothing factor, slower to respond",
    "Linear regression estimates daily weight change, provides date estimate but assumes constant rate"
  ],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}