Abstract: Described in various embodiments herein is a technical solution directed to decomposition of time as an input for machine learning, and various related mechanisms and data structures. In particular, specific machines, computer-readable media, computer processes, and methods are described that are utilized to improve machine learning outcomes, including, improving accuracy, convergence speed (e.g., reduced epochs for training), and reduced overall computational resource requirements. A vector representation of continuous time containing a periodic function with frequency and phase-shift learnable parameters is used to decompose time into output dimensions for improved tracking of periodic behavior of a feature. The vector representation is used to modify time inputs in machine learning architectures.

Abstract: Systems and methods for expanding a multi-relational data structure tunable for generating a non-linear dataset from a time-dependent query. The systems include a processor and a memory. The memory may store processor-executable instructions that, when executed, configure the processor to: receive the query of the multi-relational data structure, wherein the query includes at least one entity node at a queried time relative to the time data; obtain, based on the query, a temporal representation vector based on a diachronic embedding of the multi-relational data structure, the diachronic embedding based on a combination of a first sub-function associated with a temporal feature and a second sub-function associated with a persistent feature; determine, from the temporal representation vector, at least one time-varied score corresponding to the queried time; and generate a response dataset based on the at least one time-varied score determined from the temporal representation vector.

Abstract: Systems and methods for expanding a multi-relational data structure tunable for generating a non-linear dataset from a time-dependent query. The systems include a processor and a memory. The memory may store processor-executable instructions that, when executed, configure the processor to: receive the query of the multi-relational data structure, wherein the query includes at least one entity node at a queried time relative to the time data; obtain, based on the query, a temporal representation vector based on a diachronic embedding of the multi-relational data structure, the diachronic embedding based on a combination of a first sub-function associated with a temporal feature and a second sub-function associated with a persistent feature; determine, from the temporal representation vector, at least one time-varied score corresponding to the queried time; and generate a response dataset based on the at least one time-varied score determined from the temporal representation vector.

Abstract: Described in various embodiments herein is a technical solution directed to decomposition of time as an input for machine learning, and various related mechanisms and data structures. In particular, specific machines, computer-readable media, computer processes, and methods are described that are utilized to improve machine learning outcomes, including, improving accuracy, convergence speed (e.g., reduced epochs for training), and reduced overall computational resource requirements. A vector representation of continuous time containing a periodic function with frequency and phase-shift learnable parameters is used to decompose time into output dimensions for improved tracking of periodic behavior of a feature. The vector representation is used to modify time inputs in machine learning architectures.