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a Python toolbox for machine learning on Partially-Observed Time Series
The main features of wenjiedu/pypots are: Data Processing Tools, Analysis Toolkits.
Projects with overlapping indexed features include: aeon-toolkit/aeon — A toolkit for time series machine learning and deep learning. jpmens/jo — Jo is a command-line utility designed to construct and manipulate JSON objects and arrays directly from shell… alexbol99/flatten-js — flatten-js is a javascript library for manipulating abstract geometrical shapes like point, vector, line, ray,… aistream-peelout/flow-forecast — Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood… agitter/single-cell-pseudotime. 599yongyang/datasetloom.
A toolkit for time series machine learning and deep learning
Jo is a command-line utility designed to construct and manipulate JSON objects and arrays directly from shell arguments and standard input. It functions as a data processing tool that transforms raw input into structured formats, enabling the generation of complex payloads for APIs, configuration files, and automated data pipelines. The tool distinguishes itself through its ability to resolve hierarchical data structures using delimiter-based path definitions and its integrated type-inference engine, which automatically casts input values into native boolean, numeric, or null types. Users can