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As others have stated, you need to have a common frequency of measurement (i.e. the time between observations). With that in place I would identify a common model that would reasonably describe each s …

I will try and respond to the gentle urging of whuber to simply “respond to the question” and stay on topic. We are given 144 monthly readings of a series called “The Airline Series” . Box and Jenkins …

Testing if a series is stationary versus non-stationary requires that you consider a sequence of alternative hypotheses, one for each listable Gaussian Assumption. One has to understand that the Gauss …

The problem with fitting seasonal ARIMA to daily data is that the "seasonal component" may only operate on the weekends or maybe just the weekdays thus overall there is a non-significnat "seasonal com …

The issue here is to get to an equation that parses the observed data to signal and noise. If your data is simple then your regression approach might work. Care should be taken to understand some of t …

The sole reliance on the ACF and PACF using tools suggested in the mid 60's is sometimes but seldomly correct except for simulated data. Model Identification tools like AIC/BIC almost never correctly …

My suggested approach encompasses models that are much more general than ARIMA as they include the potential for seasonal dummies that may change over time , multiple levels ,multiple trends , paramet …

As you said "The PACF values are the coefficients of an autoregression of the series of interest on lagged values of the series" and I add where the PACF(K) is the coefficient of the last (kth) lag. T …

Extract the roots of the polynomial. If all the roots are outside the unit circle then the process is stationary. Model identification aids can be found on the web. Fundamentally the pattern of the AC …

To restate your question “ How does the analyst deal with long periods of no demand that follow no specific pattern?” The answer to your question is Intermittent Demand Analysis or Sparse Data Analys …

My answer is really an abridgement of javlacelle's but is is too long for a simple comment but not too short to be useless. While jvlacelle's response is technically correct at one level it "overly s …

looking at plots in order to try to pigeonhole the data into a guessed arima model works well when 1: There are no outliers/pulses/level shifts, local time trends and no seasonal deterministic pulses …

The correlation coefficient between time series is useless. See CORRELATION COEFFICIENT - Critical values for Testing Significance. This was first pointed out by U. Yule in 1926 Yule, G.U, 1926, "Why …

There are no known/suspected predictor variables There are no level shifts There are no deterministic time trends of the form $1,2,3,...,t$ There are no seasonal dummies There are no one time anoma …

Change Points can arise from a number of possible causes. Each of the possible causes can be evaluated. In terms of increasing complexity : 1. detecting a change in the expected value is essentially I …