Exponentially Smoothed Diet

Body weight prediction based on irregular measurements over five weeks.
Green is the predicted path in the next ten days.

Exponential smoothing is a technique that can be applied to time series data, either to produce smoothed data for presentation, or to make forecasts. The time series data themselves are a sequence of observations. The observed phenomenon may be an essentially random process, or it may be an orderly, but noisy, process. Whereas in the simple moving average the past observations are weighted equally, exponential smoothing assigns exponentially decreasing weights over time.
[http://en.wikipedia.org/wiki/Exponential_smoothing]


I applied Exponential smoothing to the time series I obtained from my livestrong.com recordings. Of course I have the highest motivation to beware this noble technique from being wrong.
Find all the R code and data below.

	# Exponential Smoother for Weight Prediction
	# Author: roland
	###############################################################################
	require(zoo)
	require(ggplot2)
	require(forecast)
	weightData <- read.zoo("weightData", format="%b %d %Y",sep=":")
	qplot.zoo <- function(x) {
	if(all(class(x) != "zoo")) stop("x must be a zoo object")
	# merge your data with an empty zoo object that has an index value for
	# every period you're interested in.
	x <- merge(x, zoo(order.by=seq(start(x), end(x), by="days")))
	# interpolate missing values
	x <- na.spline(x)
	x.df <- data.frame(dates=index(x), weight=coredata(x))
	# exponential smoothing
	fit <- ets(x)
	predicted <- forecast(fit)
	forecast <- with(predicted, data.frame(
	weight = as.numeric(mean),
	upper = as.numeric(upper),
	lower = as.numeric(lower),
	dates = max(x.df[["dates"]]) + seq_along(mean)
	))
	x.df$upper <- x.df$weight
	x.df$lower <- x.df$weight
	df <- rbind(x.df, forecast)
	ggplot(df, aes(dates, weight)) +
	geom_smooth(aes(ymin = lower, ymax = upper), stat = "identity",
	colour = "black", fill = "green")
	}
	qplot.zoo(weightData)

view raw ExponentialWeight.R hosted with ❤ by GitHub

	Jun 19 2011:83.9864760385
	Jun 20 2011:84.0007710707
	Jun 22 2011:83.5018194633
	Jun 23 2011:82.9983319322
	Jun 24 2011:82.4993803248
	Jun 27 2011:82.2997996818
	Jun 29 2011:81.301896467
	Jun 30 2011:81.5014771099
	Jul 2 2011:82.0004287174
	Jul 3 2011:80.4990379714
	Jul 6 2011:80.9979895788
	Jul 7 2011:80.199667007
	Jul 8 2011:80.000086364
	Jul 9 2011:79.5011347566
	Jul 11 2011:80.000086364
	Jul 12 2011:80.000086364
	Jul 13 2011:79.5011347566
	Jul 14 2011:79.1019734706
	Jul 15 2011:79.2017637921
	Jul 16 2011:78.498695618
	Jul 18 2011:77.9997440106
	Jul 20 2011:77.9997440106

view raw weightData hosted with ❤ by GitHub