Dangers of over-interpretation
Very strong autocorrelation in a time series can result in apparent trends, even when there is no real trend underlying the series. An important example of this is the 'random walk' model for share price data. Autocorrelation and trend are especially difficult to distinguish in short time series. You should therefore be wary of extrapolating a trend into the future if there is high autocorrelation.
The diagram below shows a time series that has been artificially generated with no trend and with successive values that are not autocorrelated.
Click New sample a few times to observe the types of pattern that might be observed with an uncorrelated time series.
Drag the slider to increase the autocorrelation coefficient to 0.95 then take a few more samples to observe the types of pattern that may be found in autocorrelated data. Observe that some series appear to exhibit trend even though the none underlies the data.
(Try making the autocorrelation coefficient negative. With negative autocorrelation, positive values tend to be followed by negative ones and vice versa. This type of series is rarely encountered in practice, although it can happen when there is an exaggerated feedback mechanism causing automatic adjustment towards an equilibrium level.)