The question is not about whether it is a monthly or a weekly data, but about how quickly the seasonality evolves. If you think the seasonal pattern is constant through time, you should set this ...

Estimating the standard deviation of a distribution requires to choose a distance. Any of the following distance can be used: $$d_n((X)_{i=1,\ldots,I},\mu)=\left(\sum | X-\mu|^n\right)^{1/n}$$ We ...

Both measure the dispersion of your data by computing the distance of the data to its mean. the mean absolute deviation is using norm L1 (it is also called Manhattan distance or rectilinear distance) ...

These concepts have been created to deal with regressions (for instance correlation) between non stationary series. Clive Granger is the key author you should read. Cointegration has been introduced ...

I suggest you read the dlm vignette http://cran.r-project.org/web/packages/dlm/vignettes/dlm.pdf especially the chapter 3.3

You can do roughly the same with the 2. Matlab is maybe cleaner and easier to use as you have only one clean library of function for each task, R is maybe more flexible as you have a LOT of ...

For the coloring, either you specify a list of colors or you generate them. In the latter, I suggest you execute this code n = 32; main.name = paste("color palettes; n=",n) ch.col = c("rainbow(n, ...

1/ do you already know the bias n? 2/ if yes (actually you need only to know which side of the coin is heavier) then you cannot do better than always bet on this side. In the long term, you'll have ...

I would say, doing tests and regressions on a small set of data. Edit: Without looking at the confidence intervals, or when the confidence intervals/error bars are not easy to calculate.

The Unscented Kalman Filter is a type of non linear Kalman filter. (ie when the transition and observation functions are non linear) If these functions are differentiable, one can simply use the ...

Do you know where does the noise comes from? Before doing any statistical test, you think about the origin of the noise you want to remove. Additive noise is independent from the level of the signal, ...

I suggest you buy the excellent book by G. Petris, S. Petrone and P. Campagnoli Dynamic Linear Models with R. You will learn that any ARMA model $Y_t = \sum_{j=1}^{r}\phi_jY_{t-j} + \sum_{j=1}^{r-1}... View answer 4 votes library(zoo) x=c(4, 5, 7, 3, 9, 8) rollmean(x,3) or library(TTR) x=c(4, 5, 7, 3, 9, 8) SMA(x,3) View answer 4 votes How do you define correlation for non stationary time series? Do you plan to take the correlation of the diff or these time series? If not, I suggest you look for cointegration rather than correlation ... View answer Accepted answer 3 votes It seems like you need the package xts. Create your time serie using install.packages('xts') library(xts) X = xts(coredata(DF[,2]), order.by=DF[,1]) Then you will be able to manipulate your data ... View answer 2 votes Detrending can be done by applying a low pass filter that calculates the trend. The remaining part is your detrended data. Two examples of low pass filters here: apply a Hodrick-Prescott filter. I ... View answer Accepted answer 2 votes Commonly, a time series is said to be$I(0)$if the time series itself is stationary (no need to differentiate it to obtain stationarity). The Wikipedia page you mention says that not all$I(0)\$ ...

I recommend Tinn-R (Which is the acronym for Tinn is not Notepad)

In the context of time series (but why would you use one side filters otherwise?): One side filters: because they only use past data, these filters can be used for backtesting and for online analysis ...

Package MethComp has a function Deming which is performing this regression http://cran.r-project.org/web/packages/MethComp/MethComp.pdf The Deming regression is a special example of the total least ...

If you use R you can maybe 1/ merge the 2 time series 2/ carry forward the values except if the delay is too long (kind of enhanced na.locf)

In my opinion, if you don't code yourself the test, you are prone to errors and misunderstandings of the results. I think that you should recommend them to hire a statistician that has computer ...

a sample representative of population cannot be obtained through internet as you will only get people interested in answering your survey online, which will give you a biased sample.

Sounds like you'll need a HMM to do that. Have you read Lawrence R. Rabiner (February 1989). "A tutorial on Hidden Markov Models and selected applications in speech recognition" There are a few ...

It is exactly the same! You want the constraint to be respected, and you don't care about the sign of g(x,y)

Bayesian networks are perfect for online estimation, and offer a great diversity of models.

I would recommend "Time Series Analysis and its applications with R examples" by Shumway and Stoffer The third edition: http://www.stat.pitt.edu/stoffer/tsa3/ Click and buy http://www.amazon.com/...