Systems And Methods For Detecting Long Term Seasons

A method comprising:

• generating a plurality of training datasets including a first training dataset comprised of observed data from the time-series signal and a second training dataset comprised of outlier data from the time-series signal;
• and
• training at least one machine learning model to predict contributions of at least a first seasonal pattern and a second seasonal pattern to values of a time-series signal;
• wherein the second seasonal pattern has a longer seasonal period than the first seasonal pattern;
• wherein the at least one machine learning model includes at least one of a short-term seasonal model or model component that is trained using the observed data in the first training dataset;
• wherein the at least one machine learning model includes at least one of a long-term seasonal model or model component that is trained using the outlier data in the second training dataset.

One or more non-transitory computer-readable media storing instructions that, when executed by one or more hardware processors, cause operations comprising:

• generating, by at least one machine learning model, a forecast predicting a behavior of at least one computing resource;
• wherein generating, by the at least one machine learning model, the forecast includes:
• estimating, for a timestamp in the forecast, a first contribution of a first seasonal pattern having a first seasonal period;
• estimating, for the timestamp in the forecast, a second contribution of a second seasonal pattern having a second seasonal period that is longer than the first seasonal period;
• and
• estimating, using the first contribution and the second contribution, a value for the forecast at a point in time corresponding to the timestamp;
• and
• performing at least one operation based on the forecast predicting the behavior of the at least one computing resource.