Hello everyone in this video tutorial
we are going to look into the time series application
of the neural network toolbox
so it can be open in 3 ways
type
and click on time series
or go to the app step and click on
neural network time series
or you can directly type
so as you can see introduction window come up
so you can read introduction
in neural network time series application is used
for those cases
when output is depended upon previous value of itself
or any other time series
so there are 3 cases possible
we have non linear auto regressive with external input
non linear auto regressive or simple non linear input
the first case means we have
neural network and output depends on its previous value
as well as previous values of anther time series
that's why it is written external here
in case of non linear auto regressive
the output only depends upon the previous value of itself
that is external time series
non linear input output is similar to
the only difference is that we don't have previous time step of the output
time series
so in this case we are only supplying the external time series
and predicting the output time series
the accuracy of non linear input output is comparatively less
as compare to
click next
in this window we need to select input and targets
click on load example data set
click on
in this we have input and target time series
the input time series represent the % of all opening
and target time series represent flow
so click on import
as you can see i have selected the cell column
what it means is that 2 cell are created for
the input and target time series
each column represent the different time step
and for each cell we can store the different sample value
for particular time step
so in this case we have 1801 time step
and only 1 sample
click on next
in this window you can divide the data set available
and training, validation and testing
let us keep it default click on next
now in this window you need to define number of hidden layers neurons
for the single hidden layer and the number of delays
i will go with the default values
what this made by delays
delays represents the number of time step
that the network hold so that it can used it
for the future prediction of the output time series
click on next network is created
now you can choose the training algorithm
that means
and click on
after the network is trained
the window will look like this
few of the parameters are like performance training states histogram regression
are similar as in the case of data fitting neural network
let us look in the time series neural network
this shows the graph between the output and target
for both training and test
if we would have selected train scg and train lm
then it would also show the validation data
so in the bottom window we can see the yellow lines
which represents the error as specified by the
you can magnify the graph clicking on tools
and zoom in
and check the individual samples
what is made by error auto correlation
this graph shows how much error is related to Lag version of
itself so at 0 Lag
error is highly related to itself
the magnitude of this is shown by mean square error
for other values the co relation should lies between
the confidence represented by the
red plotted line that is not the case then we
need to vary the number of delays or number of hidden layer neurons
to make sure the correlation lie within the
confidence level
click on input error cross co relation
value of cross co relation is between the
input and the error should be minimum
or closed to the black line that is it should be lie
between the confidence limit of 95%
represented by red dotted line
if it is not so then we need to change
value of number of delay and number of hidden layer neurons
this is the cross co relation graph
high value of cross co relation
thus we can retrain the network
or we can change the number of delays and then train the network again
let us keep the number of delays as 6
click on next
now you can see the values has
reduced slightly most of them are within the confidence limit
for auto co relation
for input output co relation also
values have decreased you can keep
varying the result or check the co relation graph
to get the significant delays and the
feedback delays
click on next
you can retrain the network by adjusting the network size
number of delays and importing larger
data set
click on next
this are the similar function as in the case of
data fitting neural network click on next
click on simple script
click on save result
as you can see the error cell the output cell
the input structure and network has been created
script for creating the time series neural network
will be discussed in the next video
click on finish
now restart the tool
now let us look at non linear auto regress
click on next
as you can see we only need to
supply target cell so click on load example data set
and let local
it depend 290 measurement
of global ice volume click on import
click on next
next
this is the same window that appearing in previous case
so
let us keep
click on next
as you can see there in only error auto co relation
and no input error co relation
as there is no input available
and this is very near to the
confidence limit you can vary the number of delays or
retrain the network to get even batter result
this shows the time series plot
and as you can see the length of the yellow lines
is comparatively very less
this means the network is trained
click on next
this is the same window as in the previos case
you can generate the simple script
as you can say the previous one was
so in this case it is
you can save the result
and click on finish
script for both these function and the
discussion of the open loop close loop
and time delay network will be discussed in next video
so that's it everyone this is it for this video
if you like it please like subscribe and share
and thanks for watching
