A computer-implemented method for real-time detection of anomalies in a time series ; wherein the method comprises the following steps: a) obtaining a next value of the time series; and b) updating (303, 323, 130-133, 230-233) a plurality of derived time series (110-113, 210-213, 410-416, 460-466) thereby obtaining a next derived value (150-153, 250-253) for one or more of the derived time series; and wherein the plurality of derived time series corresponds to one or more multi-resolution transformations based on the time series; and c) predicting , for one or more resolutions of the derived time series, a future derived value (320, 321, 170-173 270-273) of the derived time series and a confidence indicator (180-183, 170-173) for the future derived value; and d) retrieving, for one or more resolutions of the derived time series, a previously predicted derived value and a confidence indicator of the previously predicted derived value as a prediction of the next derived value; and e) detecting a change point when the difference between the next derived value and the previously predicted derived value is larger than indicated by the confidence indicator of the previously predicted derived value; and f) detecting an anomaly in the time series based on the detection of one or more change points at the one or more resolutions of the derived time series; characterized in that : the one or more multi-resolution transformations based on the time series comprises: - a first multi-resolution transformation of the time series thereby obtaining a first set (110-113) of derived time series; and - a second multi-resolution transformation of absolute values of differences between consecutive values of the time series thereby obtaining a second set (210-213) of derived time series. a) obtaining a next value of the time series; and b) updating (303, 323, 130-133, 230-233) a plurality of derived time series (110-113, 210-213, 410-416, 460-466) thereby obtaining a next derived value (150-153, 250-253) for one or more of the derived time series; and wherein the plurality of derived time series corresponds to one or more multi-resolution transformations based on the time series; and c) predicting , for one or more resolutions of the derived time series, a future derived value (320, 321, 170-173 270-273) of the derived time series and a confidence indicator (180-183, 170-173) for the future derived value; and d) retrieving, for one or more resolutions of the derived time series, a previously predicted derived value and a confidence indicator of the previously predicted derived value as a prediction of the next derived value; and e) detecting a change point when the difference between the next derived value and the previously predicted derived value is larger than indicated by the confidence indicator of the previously predicted derived value; and f) detecting an anomaly in the time series based on the detection of one or more change points at the one or more resolutions of the derived time series; - a first multi-resolution transformation of the time series thereby obtaining a first set (110-113) of derived time series; and - a second multi-resolution transformation of absolute values of differences between consecutive values of the time series thereby obtaining a second set (210-213) of derived time series. <EOS>