Question:

What are some good libraries/tools in Python for generating synthetic time series data from a given sample data set?

Example:

Given a data set of sales from January to June, what are some tools/libraries in Python to generate synthetic time series data for the remaining months while preserving the time series characteristics such as trend and seasonality?

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# Original Question
Are there any good library/tools in python for generating synthetic time series data from existing sample data? For example I have sales data from January-June and would like to generate synthetic time series data samples from July-December )(keeping time series factors intact, like trend, seasonality, etc).
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Answer:

Some good libraries/tools in Python for generating synthetic time series data from a given sample data set are:

- Pandas - has capabilities for generating synthetic time series data based on a range of statistical models and distributions.
- NumPy - has functions for generating synthetic time series data based on various distributions.
- Scikit-learn - has modules for time series analysis and generation, including seasonal decomposition and ARIMA modeling.
- Prophet - a forecasting library from Facebook that can generate synthetic time series data based on trend and seasonality components.
- PyMC3 - a probabilistic programming library that can be used to generate synthetic time series data based on Bayesian modeling.

To generate synthetic time series data for the remaining months while preserving the time series characteristics such as trend and seasonality, one can use these libraries/tools with appropriate statistical models and distributions.