A method, performed by a network optimization function associated with a wireless network, of forecasting performance issues associated with cells (106, 111, 116) in the wireless network, the method comprising: for each of a plurality of cells in the wireless network, obtaining first time-series data representing a plurality of key performance indicators, KPIs, at a first plurality of past time points; applying a multi-layer forecasting model to the first time-series data to determine second time-series data representing the plurality of KPIs for each of the plurality of cells at a second plurality of future time points; the method being characterized by comprising based on the second time-series data, determining respective probabilities of occurrence for a respective plurality of different performance issues, associated with the plurality of cells, during one or more future time periods that include the second plurality of future time points, and further characterized in that applying the multi-layer forecasting model to the first time-series data comprises: for each particular KPI, applying a first layer to predict a linear pattern in the first time-series data; for each particular KPI, applying a second layer to predict a periodic pattern in the first time-series data; and applying a third layer to predict further patterns in the plurality of KPIs based on the first time-series data, the predicted linear patterns, and the predicted periodic patterns, wherein multiple KPIs are considered together to predict them simultaneously. for each of a plurality of cells in the wireless network, obtaining first time-series data representing a plurality of key performance indicators, KPIs, at a first plurality of past time points; applying a multi-layer forecasting model to the first time-series data to determine second time-series data representing the plurality of KPIs for each of the plurality of cells at a second plurality of future time points; the method being characterized by comprising based on the second time-series data, determining respective probabilities of occurrence for a respective plurality of different performance issues, associated with the plurality of cells, during one or more future time periods that include the second plurality of future time points, and further characterized in that applying the multi-layer forecasting model to the first time-series data comprises: for each particular KPI, applying a first layer to predict a linear pattern in the first time-series data; for each particular KPI, applying a second layer to predict a periodic pattern in the first time-series data; and applying a third layer to predict further patterns in the plurality of KPIs based on the first time-series data, the predicted linear patterns, and the predicted periodic patterns, wherein multiple KPIs are considered together to predict them simultaneously. for each particular KPI, applying a first layer to predict a linear pattern in the first time-series data; for each particular KPI, applying a second layer to predict a periodic pattern in the first time-series data; and applying a third layer to predict further patterns in the plurality of KPIs based on the first time-series data, the predicted linear patterns, and the predicted periodic patterns, wherein multiple KPIs are considered together to predict them simultaneously.