Hello, I got a good inspiration from Gretel. I encountered several problems in my work.
As we all know, DGAN model needs to input multiple time series data, and the output is also multiple time series data. (Once I thought DGAN could generate a long series of time series).
My downstream task is to use the generated data to expand the original data. I made statistics on multiple time series data generated by DGAN, and found that the distribution of these data is very different from the original data, which is different from what I expected. (I hope that after merging the generated multiple time series data, it still conforms to the distribution of the original data)
I saw in Gretel's blog that the distribution of raw data and generated data match, and the results in the blog are also the results I want to achieve.
【 https://gretel.ai/blog/creating-synthetic-time-series-datahttps://github.com/gretelai/gretel- 】
【synthetics/blob/master/examples/timeseries_dgan.ipynb】
I look forward to your guidance and reply.
Hello, I got a good inspiration from Gretel. I encountered several problems in my work. As we all know, DGAN model needs to input multiple time series data, and the output is also multiple time series data. (Once I thought DGAN could generate a long series of time series). My downstream task is to use the generated data to expand the original data. I made statistics on multiple time series data generated by DGAN, and found that the distribution of these data is very different from the original data, which is different from what I expected. (I hope that after merging the generated multiple time series data, it still conforms to the distribution of the original data) I saw in Gretel's blog that the distribution of raw data and generated data match, and the results in the blog are also the results I want to achieve. 【 https://gretel.ai/blog/creating-synthetic-time-series-data https://github.com/gretelai/gretel- 】 【synthetics/blob/master/examples/timeseries_dgan.ipynb】 I look forward to your guidance and reply.