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 (160, 260) based on the time series; and c) predicting (304, 324), 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 (328, 329) and a confidence indicator of the previously predicted derived value as a prediction of the next derived value; and e) detecting (306, 326) 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 (306, 326) 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 (160, 260) based on the time series comprises: - a first multi-resolution transformation of the time series thereby obtaining a first set 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 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 (160, 260) based on the time series; and c) predicting (304, 324), 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 (328, 329) and a confidence indicator of the previously predicted derived value as a prediction of the next derived value; and e) detecting (306, 326) 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 (306, 326) 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 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 of derived time series.