time series generation

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papersTODAY 04:00 UTC

CodeTS Generates Time Series from Text via Executable Code

A new arXiv paper introduces CodeTS, a method that turns natural-language descriptions into time series by generating and running executable code rather than sampling outputs directly. This design makes the resulting synthetic data verifiable and suited to scenarios where real observations are scarce or expensive to collect. The work appears in the cs.LG and cs.AI listings.

research/time-series/code-generation/synthetic-data

papersSEP 12 04:00 UTC

LoaDiff: Conditional Generation of Electricity Consumption Time Series

A new arXiv preprint introduces LoaDiff, a method for conditionally generating residential electricity consumption time series. The work is motivated by the energy transition, where distributed generation, electrified appliances and demand-response programs are shifting how households use power. The authors argue that granular synthetic consumption data can support energy analytics; the abstract is truncated in this report.

time-series/energy/generative-models/arxiv