What is the meaning of each element in input_shape of Conv1D in Keras?
X[train].shape[0] - This is the number of instances. Let's say it is M
X[train].shape[1] - This is the shape of each  instance. Each instance is (1 x N)
Since input instances are of 1-D, the  input data become m x N. Had it been 2-D, it would have been m x Nx x Ny

And one more question, I know that CNN required fixed input size. But I split my data into train and validation data as k parameter = 5, mean that my data will become: Train = (84,95) and Validation = (21,95). So with the same model how can we train and validate data with difference size

Size means the size of each instance which is same in this case i.e (1,95). Just the number of instances are different i.e. 21 in Test and 84 in Train.
TimeSeries data -
 In TS learning, our initial data is just a time-based sequence of individual datapoint. We then make it Feature and Target by using a certain points (e.g. M) as Feature and  (M+1)th as Target. M is identified based on TS characteristics.
e.g.
1,2,3,4,5 will be (If I use 2 Feature)
1,2(X) --&gt; 3(Y)
2,3(X) --&gt; 4(Y)
3,4(X) --&gt; 5(Y) etc.
These are 3 train records.
May read this too - Machinelearningmastery