An abnormality determination apparatus comprising: a classification processing circuit configured to classify a plurality of input time-series data corresponding to a plurality of process amounts generated in a target facility into a plurality of groups; a first inference processing circuit configured to apply one or more input time-series data included in each of the groups to a first dimension reduction and restoration models that differ for each of the plurality of groups and to output one corresponding output time-series data for each input time-series data; a second inference processing circuit configured to apply a plurality of input difference data, which are the respective differences between each of the plurality of output time-series data on the plurality of process amounts and the corresponding input time-series data, to a single second dimension reduction and restoration model, and to produce a plurality of output difference data for each input difference data; a determination processing circuit configured to determine an abnormality or a sign of abnormality of the target facility, based on comparison between a plurality of addition data, which are on the respective sum of each of the plurality of output difference data and the corresponding output time-series data, and the corresponding input time-series data; and at least one of a display device configured to display the determination result indicating presence or absence of an abnormality or sign of abnormality, and a communication device for performing information communications with an external device connected to the abnormality determination apparatus via a network; wherein each of the plurality of first dimension reduction and restoration models is a machine learning model trained to restore normal input time-series data regarding the respective process amount, and the second dimension reduction and restoration model is a machine learning model trained to restore a plurality of normal input difference data. a classification processing circuit configured to classify a plurality of input time-series data corresponding to a plurality of process amounts generated in a target facility into a plurality of groups; a first inference processing circuit configured to apply one or more input time-series data included in each of the groups to a first dimension reduction and restoration models that differ for each of the plurality of groups and to output one corresponding output time-series data for each input time-series data; a second inference processing circuit configured to apply a plurality of input difference data, which are the respective differences between each of the plurality of output time-series data on the plurality of process amounts and the corresponding input time-series data, to a single second dimension reduction and restoration model, and to produce a plurality of output difference data for each input difference data; a determination processing circuit configured to determine an abnormality or a sign of abnormality of the target facility, based on comparison between a plurality of addition data, which are on the respective sum of each of the plurality of output difference data and the corresponding output time-series data, and the corresponding input time-series data; and at least one of a display device configured to display the determination result indicating presence or absence of an abnormality or sign of abnormality, and a communication device for performing information communications with an external device connected to the abnormality determination apparatus via a network; wherein each of the plurality of first dimension reduction and restoration models is a machine learning model trained to restore normal input time-series data regarding the respective process amount, and the second dimension reduction and restoration model is a machine learning model trained to restore a plurality of normal input difference data. <EOS>