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Capturing seasonality in multiple regression for daily data would be a good place to start. Essentially when you form a predictive model you can forecast/simulate future periods. The simulation is bas …

I this is time series Histograms provide little or no information/clues as to the underlying model. The answer to your questions is fairly simple of this is time series data , fit an ARIMA model takin …

If you have the data then you can compute a histogram approximating the probability density function (pdf) also known as a frequency distribution . Drawing random numbers from this pdf will not genera …

I used the data in http://pymc-devs.github.com/pymc/tutorial.html#an-example-statistical-model . It has 111 years of data . An appropriate model detects a Level Shift at 1887 . A level shift is one ki …

In the spirit of the sage comment by BB "However, the arrival rate may vary (e.g. by day of the week, time of day, and so on.)" , I suggest that you present the data for the 22hours in terms of 22x60 …

It is a little late ..... but , review the Slutsky Effect where a linear (weighted ) combinations of i.i.d. values leads to a series with auto-correlative structure. This is why assuming any filter pi …

We have seen this question ( or one like this ) before . It involves using daily data to compute aggregated forecasts yielding the probability of making a goal. Look at Predict number of users for a d …

Was it 17 consecutive days that data was not collected ? I no then I would linearly interpolate (while holding my breath !) to get estimates of these 17 missing values. I would then identify a useful …

I have successsfully detected a Level Shift in series like this using Tsay's procedures http://www.unc.edu/~jbhill/tsay.pdf. His procedures although initially directed to time series are general enoug …

My answer/comments here (way too long for a SE comment ) don't and are not meant to respond to the particular question BUT as BACON once opined "To ask the proper question is half of knowing" . I am s …

If you assume a model form that is non-linear but can be transformed to a linear model such as $\log Y = \beta_0 + \beta_1t$ then one would be justified in taking logarithms of $Y$ to meet the specifi …

SARIMA model https://autobox.com/pdfs/SARMAX.pdf identification is the answer following the ITERATIVE process here https://autobox.com/pdfs/ARIMA%20FLOW%20CHART.pdf . I strongly suggest that you consi …

You are observing transactions of people becoming registered users. These transactions can be bucketed /grouped into time intervals or time buckets. Develop either a causative model or a mwmory +fixed …

I have often used a scheme (Intervention Detection) even though it is not time series data to determine the presence of "an intercept change" or a change in the mean value. An intercept change is esse …

For each category ; for each person .... develop a robust ARIMA model for their annual absolute spending incorporating unknown deterministic structure (pulses/level shifts/local time trends) . Now ass …