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Now I got some time-series data as showing example below: enter image description here

I have spent a period of time realizing the QAE from qiskit, and I found that the tutorial used uncertainty model to reproduce data, while I want it to become my time-series data.

So here's the main question, how should I do to transform it? Can anyone helps me, please?

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  • $\begingroup$ Have you tried to use pandas directly to load your data ? specifically from a local csv file as for example : # pandas.csv_read("s3.amazonaws.com/assets.datacamp.com/blog_assets/aapl.csv", header=0, index_col= 0, names=['Open', 'High', 'Low', 'Close', 'Volume', 'Adj Close'], parse_dates=True). $\endgroup$ Jun 8 at 13:15
  • $\begingroup$ Qiskit provides data providers for financial data, and one of them is "RandomDataProvider" that generates pseudo-random mock stock-market data. I think that's what you mean when you say "uncertainty model to reproduce data" I guess you were referring to this notebook: quantum-computing.ibm.com/lab/files/qiskit-tutorials/qiskit/… $\endgroup$ Jun 8 at 13:18
  • $\begingroup$ Hi Mensac, I know the data providers you mentioned, but now I just wonder how to transform them into QAE algorithm's input. According to the tutorial, it loads a model called "GaussianConditionalIndependenceModel", and input some parameters to get several random data, which I cannot control. you can see this link: qiskit.org/documentation/tutorials/finance/… Hope you know the answar to my question haha. $\endgroup$
    – RonaldHo
    Jun 9 at 2:12
  • $\begingroup$ what do you want to use QAE for? what statistic of you data are you trying to get? $\endgroup$
    – Ciamar
    Jun 9 at 7:38
  • $\begingroup$ As far as I know, to use QAE you'll have to turn your time series into a probability distribution that you'll need to then turn into a quantum circuit. How you need to transform it will probably deppend on what you want to do. $\endgroup$
    – Ciamar
    Jun 9 at 7:47

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