title: How can I convert datetime format in R to be read for a Time-Series prediction model?
body: <p>I have a financial stock-exchange dataset of the following kind, using dput(head(data3, 7))</p>
  <pre><code>structure(list(X = 0:6, 
                 Unnamed..0 = 0:6, 
                 date = c(&quot;2022-01-04&quot;, &quot;2022-01-04&quot;, &quot;2022-01-04&quot;, &quot;2022-01-04&quot;, &quot;2022-01-04&quot;, &quot;2022-01-04&quot;, &quot;2022-01-04&quot;), 
                 time = c(7.089, 8.619, 8.908, 9.588, 10.744, 10.931, 10.931), 
                 price = c(263, 259, 259, 260, 258, 258, 259), qty = c(2L, 1L, 2L, 2L, 2L, 2L, 3L), 
                datetime = c(&quot;2022-01-04 8:00:07.089000&quot;, &quot;2022-01-04 8:00:08.619000&quot;, &quot;2022-01-04 8:00:08.908000&quot;, &quot;2022-01-04 8:00:09.588000&quot;, &quot;2022-01-04 8:00:10.744000&quot;, &quot;2022-01-04 8:00:10.931000&quot;, &quot;2022-01-048:00:10.931000&quot;)), 
        row.names = c(NA, 7L), 
        class = &quot;data.frame&quot;)
  </code></pre>
  <p>I am trying to apply an ARIMA model to it. The columns Date and Time, for each day, uniquely identify the changes in the price of a commodity for every second of each day, for months ahead. The diagram is just shortened tremendously.</p>
  <p>In this case, How can I change this date and time to be effectively used by an ARIMA model in R/Python?</p>