Abstract: To automate time series forecasting machine learning pipeline generation, a data allocation size of time series data may be determined based on one or more characteristics of a time series data set. The time series data may be allocated for use by candidate machine learning pipelines based on the data allocation size. Features for the time series data may be determined and cached by the candidate machine learning pipelines. Predictions of each of the candidate machine learning pipelines using at least the one or more features may be evaluated. A ranked list of machine learning pipelines may be automatically generated from the candidate machine learning pipelines for time series forecasting based upon evaluating predictions of each of the one or more candidate machine learning pipelines.

Abstract: Computer hardware and/or software that performs the following operations: (i) assessing a performance of a plurality of unsupervised machine learning pipelines against a plurality of data sets; (ii) associating the performance with meta-features corresponding to respective pipeline/data set combinations; (iii) training a supervised meta-learning model using the associated performance and meta-features as training data; and (iv) utilizing the trained model to identify one or more pipelines for processing an input data set.

Abstract: An embodiment of the invention may include a method, computer program product, and system for creating a data analysis tool. The method may include a computing device that generates an AI pipeline based on an input dataset, wherein the AI pipeline is generated using an Automated Machine Learning program. The method may include converting the AI pipeline to a non-native format of the Automated Machine Learning program. This may enable the AI pipeline to be used outside of the Automated Machine Learning program, thereby increasing the usefulness of the created program by not tying it to the Automated Machine Learning program. Additionally, this may increase the efficiency of running the AI pipeline by eliminating unnecessary computations performed by the Automated Machine Learning program.

Abstract: Predefined pipelines may be created with predefined meta-features. Time series data may be segmented using lookback window parameters. Meta-features may be determined for windowed data. Those of the predefined pipelines having a maximum amount of matching predefined meta-features may be determined. Those of the lookback window parameters that result in the windowed data having the meta-features most similar to the meta-features of one or more of the plurality of predefined pipelines may be identified.

Abstract: Computer hardware and/or software that performs the following operations: (i) assessing a performance of a plurality of unsupervised machine learning pipelines against a plurality of data sets; (ii) associating the performance with meta-features corresponding to respective pipeline/data set combinations; (iii) training a supervised meta-learning model using the associated performance and meta-features as training data; and (iv) utilizing the trained model to identify one or more pipelines for processing an input data set.

Abstract: A method, system, and computer program product for a metalearner for automated machine learning are provided. The method receives a labeled data set. A set of data subsets is generated from the labeled data set. A set of unsupervised machine learning pipelines is generated. A training set is generated from the set of data subsets and the set of unsupervised machine learning pipelines. The method trains a metalearner for unsupervised tasks based on the training set.

Abstract: Techniques regarding one or more automated machine learning processes that analyze time series data are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a time series analysis component that selects a machine learning pipeline for meta transfer learning on time series data by sequentially allocating subsets of training data from the time series data amongst a plurality of machine learning pipeline candidates.

Abstract: Methods and systems for generating an environment include training transformer models from tabular data and relationship information about the training data. A directed acyclic graph is generated, that includes the transformer models as nodes. The directed acyclic graph is traversed to identify a subset of transformers that are combined in order. An environment is generated using the subset of transformers.

Abstract: In a method for ranking machine learning (ML) pipelines for a dataset, a processor receives first performance curves predicted by a meta learner model for a plurality of ML pipelines. A processor allocates a first subset of data points from the dataset to each of the plurality of ML pipelines. A processor receives first performance scores for each of the ML pipelines for the first subset of data points. A processor updates the meta learner model using the first performance scores. A processor receives second performance curves from the meta learner model updated with the first performance scores. A processor ranks the plurality of ML pipelines based on the second performance curves.

Abstract: To automate time series forecasting machine learning pipeline generation, a data allocation size of time series data may be determined based on one or more characteristics of a time series data set. The time series data may be allocated for use by candidate machine learning pipelines based on the data allocation size. Features for the time series data may be determined and cached by the candidate machine learning pipelines. Predictions of each of the candidate machine learning pipelines using at least the one or more features may be evaluated. A ranked list of machine learning pipelines may be automatically generated from the candidate machine learning pipelines for time series forecasting based upon evaluating predictions of each of the one or more candidate machine learning pipelines.

Abstract: To rank time series forecasting in machine learning pipelines, time series data may be incrementally allocated from a time series data set for testing by candidate machine learning pipelines based on seasonality or a degree of temporal dependence of the time series data. Intermediate evaluation scores may be provided by each of the candidate machine learning pipelines following each time series data allocation. One or more machine learning pipelines may be automatically selected from a ranked list of the one or more candidate machine learning pipelines based on a projected learning curve generated from the intermediate evaluation scores.

Abstract: An automated analytic tool (AAT) apparatus analyzes a machine learning system (MLS). The tool comprises a processor configured to receive experiment parameters associated with an experiment performed on the MLS, and captures information from a plurality of stages of the experiment. The information comprises information regarding MLS results and choices made during the experiment. The tool automatically revise the captured information into revised information utilizing a knowledge base comprising information from prior experiments. The tool then presents the revised information to a user.

Abstract: Techniques regarding one or more automated machine learning processes that analyze time series data are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a time series analysis component that selects a machine learning pipeline for meta transfer learning on time series data by sequentially allocating subsets of training data from the time series data amongst a plurality of machine learning pipeline candidates.

Abstract: An embodiment of the invention may include a method, computer program product, and system for creating a data analysis tool. The method may include a computing device that generates an AI pipeline based on an input dataset, wherein the AI pipeline is generated using an Automated Machine Learning program. The method may include converting the AI pipeline to a non-native format of the Automated Machine Learning program. This may enable the AI pipeline to be used outside of the Automated Machine Learning program, thereby increasing the usefulness of the created program by not tying it to the Automated Machine Learning program. Additionally, this may increase the efficiency of running the AI pipeline by eliminating unnecessary computations performed by the Automated Machine Learning program.

Abstract: Techniques for electrical power transfer in photovoltaic systems are provided. In one aspect, a photovoltaic system includes an array of photovoltaic power producing elements (e.g., concentrator photovoltaic cells); a power receiving unit; and at least one ratiometric DC to DC converter connected to both the array of photovoltaic power producing elements and the power receiving unit. The array of photovoltaic power producing elements can include a plurality of the photovoltaic power producing elements connected in series or in parallel. In another aspect, a method of transferring electrical power from an array of photovoltaic power producing elements to a power receiving unit includes the following step. At least one ratiometric DC to DC converter is connected to both the array of photovoltaic power producing elements and the power receiving unit. The at least one ratiometric DC to DC converter is configured to alter a voltage output from the array.

Abstract: A graphics processing and display system includes a coordinate sensing system for determining position and orientation of a view point reference with respect to a schematic representation of at least one three-dimensional object; a view point controller for controlling a display view point according to the determined position and orientation of the view point reference with respect to the schematic representation; and a display processor for generating pixel data representing a two-dimensional rendering of the at least one three-dimensional object according to the graphics data and display view point, wherein the two-dimensional rendering is different from the schematic representation, and wherein the pixel data generated by the display processor is output for display on a display device.