How to apply a filter or a smoothing technique to time series data (MS Excel)?

It helps to have a model of the noise process you're dealing with. Is it aditive or multiplicative? Is it time correlated? Oh well. I'll just assume it's i.i.d. additive noise of constant intensity.

My suggestion is to use cubic splines. I don't know how many data sets you have, or how many observations. With splines, you trade off fit for smoothness. At one extreme (good fit), you just get your noisy data handed back. At the other extreme (good smoothness), you get the least squares line fit. In between, you hope you can find a setting where the noise is gone, but you haven't obliterated real trends in the data. For example, if you're looking at observations of flight trajectories, you know how many g's (acceleration = second derivative) the plane/bird/whatever can pull, and maybe how much jerk (=third derivative). Sometimes, you can just eyeball the smoothed output and change the settings if you don't like what you see.

I like Carl de Boor's book _A Practical Guide to Splines_, Springer-Verlag, 1978.

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