A method comprising: ― obtaining, by one or more data processors, multiple current observations characterizing current operation of a telecommunications network, the multiple observations including performance monitoring data for the network and quality of service data for the network; ― obtaining, by the one or more data processors, one or more prior network states for the network; ― determining, by the one or more data processors using a machine learning model, a current state of the network based on the current observations and the one or more prior network states, including processing the current observations in accordance with current values of a set of machine learning model parameters; ― logging, by the one or more data processors, the current state of the network as an additional state of the one or more prior network states; and ― performing, by the one or more processors, one or more actions based on the current state of the network, including, when the current state of the network is an abnormal state, taking an action that is mapped to the abnormal state; ― wherein determining, by the one or more data processors using the machine learning model, the current state of the network comprises: ― processing the current observations in accordance with the current values of the set of machine learning model parameters to generate an output indicating the current state of the network; ― normalizing the current state of the network based on the one or more prior states of the network; ― wherein when the output of the machine learning model indicates the current state of the network is a given abnormal state; and the normalized current state of the network is a normal state, the method further comprises ― preventing an action that is mapped to the given abnormal state from being performed based on the normalized current state of the network being the normal state. ― obtaining, by one or more data processors, multiple current observations characterizing current operation of a telecommunications network, the multiple observations including performance monitoring data for the network and quality of service data for the network; ― obtaining, by the one or more data processors, one or more prior network states for the network; ― determining, by the one or more data processors using a machine learning model, a current state of the network based on the current observations and the one or more prior network states, including processing the current observations in accordance with current values of a set of machine learning model parameters; ― logging, by the one or more data processors, the current state of the network as an additional state of the one or more prior network states; and ― performing, by the one or more processors, one or more actions based on the current state of the network, including, when the current state of the network is an abnormal state, taking an action that is mapped to the abnormal state; ― wherein determining, by the one or more data processors using the machine learning model, the current state of the network comprises: ― processing the current observations in accordance with the current values of the set of machine learning model parameters to generate an output indicating the current state of the network; ― normalizing the current state of the network based on the one or more prior states of the network; ― wherein when the output of the machine learning model indicates the current state of the network is a given abnormal state; and the normalized current state of the network is a normal state, the method further comprises ― preventing an action that is mapped to the given abnormal state from being performed based on the normalized current state of the network being the normal state. ― processing the current observations in accordance with the current values of the set of machine learning model parameters to generate an output indicating the current state of the network; ― normalizing the current state of the network based on the one or more prior states of the network; ― wherein when the output of the machine learning model indicates the current state of the network is a given abnormal state; and the normalized current state of the network is a normal state, the method further comprises ― preventing an action that is mapped to the given abnormal state from being performed based on the normalized current state of the network being the normal state.