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If the number of stories per day is systematic with the day of the week , or the week of the year , or a particular day in the month , or if there has been shifts in the mean over time or changes in t …

The outliers make the sigma wide so that you miss outliers. The outlier was in fact a promotion. Using just the history of the series is not enough. … The reality is that Simple methods/software use a process where they assume a "mean model" to determine the outliers. The correct way is to build a model and identify the outliers at the same time. …

I think that i have helped procedures to do precisely what you want. Given data prior to the most recent value ...."what is the probability that the "new value" is different form what it was supposed …

EDIT: Pulse outliers are often be mis-dagnosed as variance changes. They are 1 period variance changes. THe procedures I refer to are appropriate for single series not parallel series. …

This is broadly referred to as Intervention Detection http://docplayer.net/12080848-Outliers-level-shifts-and-variance-changes-in-time-series.html which encompasses not only change point detection (level …

Anomalies can be easily detected while using "moving averages" . Please review the woRk or Tsay http://www.unc.edu/~jbhill/tsay.pdf . You might also search for "AUTOMATIC DETECTION OF INTERVENTION VAR …

hope this helps Simplistic approaches , as suggested here , often fail to their lack of generality. In general you may have a series that has multiple trends and/or multiple levels thus to detect ano …

In order to identify outliers, one needs to have an equation that adequately describes the signal. …

Outliers are a reflection of an unknown/unspecified external factor. If there is a relationship between two series then there would be an increased probability that both series would be affected. …

Outliers should always be identified and dealt with by including either a 0/1 dummy predictor variable (a form of data cleansing) or by introducing an additional (perhaps newly identified !) … The reason for this is that the empirically identified outliers enable robust parameter estimation but can severely under-estimate the going-forward uncertainty (error variance) around predictions. …

Is there a version of the correlation coefficient that is less-sensitive to outliers? discusses estimating the effect of an outlier on the correlation coefficient . …

IO=TC When you have a stationary series and you correctly have differenced it as a pulse =[1-B] STEP or a STEP=PULSE/[1-B] or a pulse = [1-.99999]STEP if the transitional coefficient is ap …

I would use Intervention Detection as discussed here http://docplayer.net/12080848-Outliers-level-shifts-and-variance-changes-in-time-series.html . … One could then rank the meters by the frequency of the different kinds of "outliers" . …

Since you have unequal intervals between readings time series methods are inapplicable. BUT I would consider using an average reading for a fixed interval of time say 60 second intervals and build/ide …

Identify an ARIMA model using all the data and then add an indicator series for the last point (0,1) to your model. Compute the t value associated with that pulse indicator. Convert the t value to a p …