Here are some recommendations:

Classic texts include:

1. Box, G. E. P. and Jenkins, G. M. "Time Series Analysis, Forecasting and Control," Holden Day, San Francisco, 1970

2. Box, G. E. P., Jenkins, G. M. and Reinsel, G. C. "Time Series Analysis, Forecasting and Control," 3rd ed., Prentice Hall, Englewood Clifs, 1994

One good elementary introduction is by: 

3. S. Makridakis, S. C. Wheelwright, and R J. Hyndman, 1998. "Forecasting methods and applications", 3rd edition, New York: John Wiley  Sons.

A recent update particularly of Kalman-Filter:

4. A survey in 2011 is by Siem Koopman and Marius Ooms, "Forecasting Economic Time Series Using Unobserved Components Time Series Model," which is Chapter 5 in "The Oxford Handbook of Economic Forecasting," edited by Michael Clements and David Hendry, 2011, Oxford University Press.

For an elementary application of the Box-Jenkins method, I suggest you download this article on the web:

http://www.jaqm.ro/issues/volume-3,issue-2/pdfs/dobre_alexandru.pdf

Dear Lall,

Thanks for the info.  I'll take a look at it.

Best regards,

Juan

Dear Juan,

The following is an  advanced DSP book with particular emphasis on noise reduction:

"Advanced Digital Signal Processing and Noise Reduction", Saeed V. Vaseghi, Wiley (3rd edt.), 2006. (ISBN: 978-0470094945)

Best regards,

Juan,

      The Kalman filter is nicely described in Andrew C. Harvey's 1989 work,  Forecasting, Structural Time Series, and the Kalman Filter.  Durbin and Koopman's 2000 book,  Time Series by State Space Methods is more compact presentation.

      However, if you want to focus on band pass filters used to distinguish trend from cycle, you should check out Hodrick and Prescott's filter article (1997) in the Journal of Money, Credit and Banking or Marianne Baxter- Robert King's work 1999 paper, "Measuring Business Cycles: Approximate Band-Pass Filters For Economic Time Series", or the 2003 International Economics Review article by Christiano-Fitzgerald on the band-pass filters. 

    Cheers,

             Bob Yaffee

Thanks a million.  I'll check them out.  We are proposing new noise filters and want to compare with standard references.

Dear Juan Flores

The singular spectrum analysis (SSA) is a good and simple method to filter time series. Look the following reference:

http://perso.ens-lyon.fr/patrick.flandrin/hassani.pdf

Singular Spectrum Analysis: Methodology and Comparison

Journal of Data Science 5(2007), 239-257

Hossein Hassani

Other interesting method is the LOESS, a non-parametric regression

https://www.census.gov/srd/papers/pdf/rrs2008-03.pdf

Muito obrigado.  Thanks a lot.

An excellent book ...

N. Morrison, Introduction to Sequential Smoothing and Prediction, McGraw Hill, New York USA, 1969; available online: http://goo.gl/CM9cN.

Interesting book.  I will take a look at it.  Thanks.

Interesting book.  I will take a look at it.  Thanks.