I have data which has the following appearance: data = {{DateList, Real}, ..., {DateList, Real}} It's easy to visualize the data by using DateListPlot. In addition to DateListPlot, I would also like to show a function which fits the data and the moving average of the data.

I hope I described the problem clear enough.

  • $\begingroup$ You can convert the dates to numbers using AbsoluteTime; DateListPlot will still work and you can also do the fit and other manipulations. $\endgroup$ Commented Oct 8, 2012 at 9:29
  • $\begingroup$ DateListPlot will actually render faster if you convert to absolute time beforehand (incl. timing for the conversion). $\endgroup$ Commented Oct 8, 2012 at 10:38

3 Answers 3


To fit a function and to calculate the moving average you need to convert your dates in absolute time using AbsoluteTime[].

data = FinancialData["IBM", "Jan. 1, 2004"];
newdata = 
    Table[{AbsoluteTime[data[[i, 1]]], data[[i, 2]]}, {i, Length[data]}];
lm = LinearModelFit[newdata, x, x];
movAvg = MovingAverage[newdata, 200];
Show[DateListPlot[newdata],DateListPlot[movAvg, PlotStyle -> Red],
    Plot[lm[x], {x, Min[newdata[[All, 1]]], Max[newdata[[All, 1]]]}], 
    Frame -> True]

DateListPlot with fit and moving average


This update implements the comment by Mike Honeychurch. Note that the moving average can be computed by averaging runs of only odd r elements.

movAvgDoneRight = MovingAverage[newdata[[All, 2]], 201];
elementsToDrop = (Length[newdata] - Length[movAvgDoneRight]);
movAvgData = Transpose[{Drop[
    Drop[newdata[[All, 1]], elementsToDrop/2], -elementsToDrop/2], 
Show[DateListPlot[newdata], DateListPlot[movAvgData, PlotStyle -> Red], 
    Plot[lm[x], {x, Min[newdata[[All, 1]]], Max[newdata[[All, 1]]]}], 
    Frame -> True]

DateListPlot with fit and moving average

  • 1
    $\begingroup$ Just a minor comment: You don't really need a table to get newdata. I find modifiying data in place using data[[All, 1]] = AbsoluteTime /@ data[[All, 1]]; more elegant :) Otherwise +1 $\endgroup$
    – Ajasja
    Commented Oct 8, 2012 at 10:08
  • 1
    $\begingroup$ @Ajasja: ...unless you have to do something after plotting/fitting that needs the date lists as opposed to the absolute times. $\endgroup$ Commented Oct 8, 2012 at 10:10
  • $\begingroup$ I'd interpret moving average in this context as meaning just of the data -- not of the dates (times) as well. $\endgroup$ Commented Oct 8, 2012 at 10:36
  • $\begingroup$ Thx, exactly what I've been looking for. $\endgroup$
    – RMMA
    Commented Oct 8, 2012 at 10:53
  • 1
    $\begingroup$ @VLC trim the dates (times) and transpose with the averaged data. $\endgroup$ Commented Oct 8, 2012 at 11:45

Another possibility is to use TradingChart or InteractiveTradingChart, which have a long list of statistical indicators :

InteractiveTradingChart[{"IBM", {"Jan. 1, 2004"}}]



For data in the form { ..., {date_i, value_i}, ... } you should consider using TimeSeries.

For example:

data = FinancialData["IBM", "Jan. 1, 2015"];


ts = TimeSeries[data]

enter image description here

And then you can operate on the time series directly, with functions like MovingAverage, and DateListPlot:

newts = MovingAverage[ts, 5]

enter image description here

DateListPlot[ newts]

enter image description here


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