Manipulating data is not a term I would use. It is common for statisticians to apply kernel density estimation or other univariate density estimation techniques to create an estimate of the population probability distribution. This assumes that a density exists for the distribution.
Although the question could be made more precise and a data example would be helpful I think it is fair to give my answer. My assumptions are as follows:
- The author has a data set which he believes is a sample from an absolutely continuous distribution and hence has a density (that presumably is a relatively smooth function).
- When the data is plotted using a histogram it is not sufficiently smooth.
These are the conditions under which smoothing of the histogram to form a probability density estimate are appropriate. Kernel density estimation is one very common way to do this. The amount of smoothing is always an issue and requires judgement. For kenrel density estimation the kernel has both a shape and a bandwidth. As Silverman points out in his book, the bandwidth is far more important than the shape even though there is theory to tell you the optimal shape. Here is an amazon link to Silverman.
Since the data are time series the Fourier transform of the autocorrelation function is spectral desnity function. Given a sample from a stationary time series you can get sample estimates for the autocorrelation function. The Forier transform of the sample autocorrelation function is called the periodogram. Due to sampling error the periodogram can appear very noisy. Just like you can smooth a histogram with a kernel smoother to estimate a probability density, so too can you smooth a periodogram to get a smooth estimate of the spectral density.
The smoothed spectral density can be transformed back to the time domain using the inverse Fourier transforn to get a smooth version of the autocorrelation function. The autocorrelation function estimate can suggest a possible ARMA model to fit the seires in the time domian. Fitting a parametric model in the time domain is one way to filter out the random white noise component of the series.
Since you mention having ten different time series that may be related (we call such series as being cross-correlated meaning that X at time t is correlated to Y at time t+h for various lead or lag time units. There are multivariate time series models particular of the autoregressive moving average form that can be used for this. Parametric forms for these model can be identified using the autocorrelation and cross-correlation function estimates.
Good references for univariate time series and multivariate time series are Box and Jenkins and Tsay for univariate and mutivariate/multiple time series respectively. Actually Tsay's text includes univariate GARCH models which are important special models for financial time series as well as other univariate models and a substantial chapter on multivariate models. Reinsel is completely devoted to multivariate time series.