A display method is for displaying estimation result data of a feature of a laser device after an estimation start timing, the estimation result data being obtained using a trained model and actual data of the laser device up to the estimation start timing. The display method includes storing the actual data and the estimation result data, generating a display screen in which temporal transition of the actual data and temporal transition of the estimation result data are connected and displayed, and displaying the display screen.

An information processing apparatus for a measurement target transitioning between a first state and a second state, comprising processing circuitry to: generate first distance information based on measurement waveform data including first data points and reference waveform data including second data points related to the first state, including distances between the first and the second data points; determine corresponding second data points for each first data point to generate first correspondence data; replace a distance between at least one target data point selected from the second data points and one or more of the first data points with a setting value to generate second distance information; determine corresponding second data points for each first data point based on the second distance information to generate second correspondence data; and detect an interval in which the measurement target is in the second state based on the first and the second correspondence data.

In an example implementation, a method includes populating a training dataset for training a machine-learning model to provide estimations associated with at least one object by obtaining a predetermined input sample comprising one or more sets of time-resolved values for one or more observables of the at least one object, generating a further input sample based on the predetermined input sample by applying a transformation over a time interval of at least one of the one or more sets of the time-resolved values of the predetermined input sample, and adding the further input sample to the training dataset to provide an augmented training dataset.

An information processing device includes an acquisition unit that acquires time-series data including a current value of a first energy storage device detected by a first detection device, and a correction unit that corrects the acquired time-series data based on a correlation between the time-series data acquired by the acquisition unit and reference time-series data including a current value of a second energy storage device detected by a second detection device.

A monitoring support system includes: sensor information acquisition processor circuitry that acquires time-series sensor information detected at one or more sensors provided in a living space of a person to be monitored; a feature amount generator that generates a feature amount based on the time-series sensor information; factor analysis processor circuitry that performs factor analysis based on the feature amount using a predetermined factor; temporal change specification processor circuitry that specifies a temporal change of the factor based on a result of the factor analysis; and a prediction information generator that generates prediction information regarding state transition of the person to be monitored based on the temporal change.

According to an embodiment, an information processing apparatus includes one or more hardware processors configured to: obtain a first feature including a feature indicating a temporal order and a second feature different from the first feature of input time-series data by using an encoder that extracts the first feature and the second feature from the input time-series data; obtain output time-series data generated based on the first feature and the second feature obtained from the input time-series data by using a decoder that generates the output time-series data based on the first feature and the second feature that are input; and train the encoder and the decoder such that a difference between the input time-series data and the output time-series data becomes small.

Systems, devices, and methods described herein may involve receiving movement data associated with a subject, the movement data collected during repetitive movement of the subject; generating a phase portrait based on the movement data; calculating a phase portrait metric of a characteristic of the phase portrait; and assigning the subject to a movement phenotype based on the phase portrait metric.

A system and method transforms conventional waveform measurements from legacy power quality meters into synchro-waveforms. The system and method does not require legacy power quality meters to be equipped with GPS receivers or other time-synchronization hardware. Data-driven methods operate in two steps: first, they perform optimization-based event signature alignment, and then they use the results to estimate a synchronization operator between any two legacy meters.

Model drift management of one or more machine learning models deployed across one or more physical systems, including executing a first process configured to detect model drift occurring on the one or more deployed machine learning models in real time, the first process configured to intake time series sensor data of one or more physical systems and one or more labels associated with the time series sensor data to output detected model drift detected from the one or more deployed machine learning models; and executing a second process configured to predict model drift from the one or more deployed machine learning models, the second process configured to intake the output model drifts from the first machine learning model and the time series sensor data to output predicted model drift of the one or more deployed machine learning models, wherein the second process is another machine learning model.

A method for time series anomaly detection includes: generating, based on multi-source time series data and contextual data, geometric trajectories representing movement of an entity; processing the geometric trajectories and the contextual data to extract a plurality of features, wherein the plurality of features include temporal features, spatial features and contextual features; generating a data structure representing semantic trajectories, wherein each of the semantic trajectories includes the temporal features, the spatial features and the contextual features; generating, using the data structure, based on the contextual features, contextual encodings corresponding to the semantic trajectories and generating, based on the temporal features, temporal encodings corresponding to the semantic trajectories; processing, with a machine learning model, the contextual encodings and the temporal encodings to generate source embeddings representing interdependencies between the semantic trajectories; and outputting, based on the source embeddings, an indication of whether one of the semantic trajectories is anomalous.