How can i convert a Time series data from table to cell array ?

I have a dataset as .csv like

Date Value

8/10/2019 11:50 1642

8/10/2019 11:55 1621

I want to use the https://au.mathworks.com/help/deeplearning/examples/time-series-forecasting-using-deep-learning.html Time Series Forecasting Using Deep Learning (LSTM) with my data set. I tried to read the table first then convert the table to cell C = table2cell(T) to match the example

data = chickenpox_dataset;
data = [data{:}];

but it didn't match. As you can see, my dataset contains time series, with time steps corresponding to Date (and time) and values corresponding to some number. How to conver this table of 2 X 100 (0r more) as a cell array, where each element is a single time step and Reshape the data to be a row vector to match the exaple? Like row 1 contains the Date and time and row 2 contains value of each date and time, so that i can experiemnt the example?

[Ganesh Regoti]

Hi Israt,

Could send a small snippet of data that would help in gaining more insight on the issue?

[Stephen23]

israt fatema's "Answer" moved here:

Answer 1

[Ganesh Regoti]

Hi Stephen,

The model which you have specified is expecting an array of numeric data. I presume that you have converted the table data to cell format but not cell to array format. Here is the code snippet that could help you solve the issue

T = readtable('test.csv');
C = table2cell(T);
C = C';
Data = [C{2,:}];

Hope this helps!