How do I identify slow decay in a seasonal time series?

I have a set of seasonal time series data and I would like to know what method I can use to determine if the data is decaying to 0 or if what I am seeing is actually part of a seasonal drop. By decay I mean that over time the metric is going to 0.

Here is a chart as an example:

How can I differentiate the gradual failure from a seasonal drop?

• Has it ever gone to 0? Is there any data on what that typically looks like, or do you just want to identify when it is dropping faster than normal? – gung Jan 29 '16 at 16:48
• No, it's never gone to 0 and I should clarify that the data I am looking at goes back 7 days and it is in 5m increments. I basically would just like to identify when the data is headed to 0 or if I am just in a seasonal drop. I have performed a Holt-Winters forecast of the data and that's nice to see but I still am having trouble differentiating. – Eric Jan 29 '16 at 16:50
• A simple linear model could give you the general trend. If you know the length of the season, try x(t) = x(t-length of season) and a lag plot could give you some simple insight. If the trend is decreasing, then you have your answer. – YCR Feb 1 '16 at 14:38