I used ntstool to create NAR (nonlinear Autoregressive) net object, by training on a 1x1247 input vector. (daily stock price for 6 years)
I have finished all the steps and saved the resulting net object to workspace.
Now I am clueless on how to use this object to predict the y(t) for example t = 2000, (I trained the model for t = 1:1247)
In some other threads, people recommended to use sim(net, t) function - however this will give me the same result for any value of t. (same with net(t) function)
I am not familiar with the specific neural net commands, but I think you are approaching this problem in the wrong way. Typically you want to model the evolution in time. You do this by specifying a certain window, say 3 months.
What you are training now is a single input vector, which has no information about evolution in time. The reason you always get the same prediction is because you only used a single point for training (even though it is 1247 dimensional, it is still 1 point).
You probably want to make input vectors of this nature (for simplicity, assume you are working with months):
[month1 month2; month2 month 3; month3 month4]
This example contains 2 training points with the evolution of 3 months. Note that they overlap.
Use the Network
After the network is trained and validated, the network object can be used to calculate the network response to any input. For example, if you want to find the network response to the fifth input vector in the building data set, you can use the following
a = net(houseInputs(:,5))
a =
34.3922
If you try this command, your output might be different, depending on the state of your random number generator when the network was initialized. Below, the network object is called to calculate the outputs for a concurrent set of all the input vectors in the housing data set. This is the batch mode form of simulation, in which all the input vectors are placed in one matrix. This is much more efficient than presenting the vectors one at a time.
a = net(houseInputs);
Each time a neural network is trained, can result in a different solution due to different initial weight and bias values and different divisions of data into training, validation, and test sets. As a result, different neural networks trained on the same problem can give different outputs for the same input. To ensure that a neural network of good accuracy has been found, retrain several times.
There are several other techniques for improving upon initial solutions if higher accuracy is desired. For more information, see Improve Neural Network Generalization and Avoid Overfitting.
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I'm trying to create a sample neural network that can be used for credit scoring. Since this is a complicated structure for me, i'm trying to learn them small first.
I created a network using back propagation - input layer (2 nodes), 1 hidden layer (2 nodes +1 bias), output layer (1 node), which makes use of sigmoid as activation function for all layers. I'm trying to test it first using a^2+b2^2=c^2 which means my input would be a and b, and the target output would be c.
My problem is that my input and target output values are real numbers which can range from (-/infty, +/infty). So when I'm passing these values to my network, my error function would be something like (target- network output). Would that be correct or accurate? In the sense that I'm getting the difference between the network output (which is ranged from 0 to 1) and the target output (which is a large number).
I've read that the solution would be to normalise first, but I'm not really sure how to do this. Should i normalise both the input and target output values before feeding them to the network? What normalisation function is best to use cause I read different methods in normalising. After getting the optimized weights and use them to test some data, Im getting an output value between 0 and 1 because of the sigmoid function. Should i revert the computed values to the un-normalized/original form/value? Or should i only normalise the target output and not the input values? This really got me stuck for weeks as I'm not getting the desired outcome and not sure how to incorporate the normalisation idea in my training algorithm and testing..
Thank you very much!!
So to answer your questions :
Sigmoid function is squashing its input to interval (0, 1). It's usually useful in classification task because you can interpret its output as a probability of a certain class. Your network performes regression task (you need to approximate real valued function) - so it's better to set a linear function as an activation from your last hidden layer (in your case also first :) ).
I would advise you not to use sigmoid function as an activation function in your hidden layers. It's much better to use tanh or relu nolinearities. The detailed explaination (as well as some useful tips if you want to keep sigmoid as your activation) might be found here.
It's also important to understand that architecture of your network is not suitable for a task which you are trying to solve. You can learn a little bit of what different networks might learn here.
In case of normalization : the main reason why you should normalize your data is to not giving any spourius prior knowledge to your network. Consider two variables : age and income. First one varies from e.g. 5 to 90. Second one varies from e.g. 1000 to 100000. The mean absolute value is much bigger for income than for age so due to linear tranformations in your model - ANN is treating income as more important at the beginning of your training (because of random initialization). Now consider that you are trying to solve a task where you need to classify if a person given has grey hair :) Is income truly more important variable for this task?
There are a lot of rules of thumb on how you should normalize your input data. One is to squash all inputs to [0, 1] interval. Another is to make every variable to have mean = 0 and sd = 1. I usually use second method when the distribiution of a given variable is similiar to Normal Distribiution and first - in other cases.
When it comes to normalize the output it's usually also useful to normalize it when you are solving regression task (especially in multiple regression case) but it's not so crucial as in input case.
You should remember to keep parameters needed to restore the original size of your inputs and outputs. You should also remember to compute them only on a training set and apply it on both training, test and validation sets.
I am trying to classify portions of time series data using a feed forward neural network using 20 neurons in a single hidden layer, and 3 outputs corresponding to the 3 events I would like to be able to recognize. There are many other things that I could classify in the data (obviously), but I don't really care about them for the time being. Neural network creation and training has been performed using Matlab's neural network toolbox for pattern recognition, as this is a classification problem.
In order to do this I am sequentially populating a moving window, then inputting the window into the neural network. The issue I have is that I am obviously not able to classify and train every possible shape the time series takes on. Due to this, I typically get windows filled with data that look very different from the windows I used to train the neural network, but still get outputs near 1.
Essentially, the 3 things I trained the ANN with are windows of 20 different data sets that correspond to shapes that would correspond to steady state, a curve that starts with a negative slope and levels off to 0 slope (essentially the left half side of a parabola that opens upwards), and a curve corresponding to 0 slope that quickly declines (right half side of a parabola that opens downwards).
Am I incorrect in thinking that if I input data that doesn't correspond to any of the items I trained the ANN with it should output values near 0 for all outputs?
Or is it likely due to the fact that these basically cover all the bases of steady state, increasing and decreasing, despite large differences in slope, and therefore something is always classified?
I guess I just need a nudge in the right direction.
Neural network output values
A neural network may not guarantee specific output values if these input values / expected output values were presented during the training period.
A neural network will not consistently output 0 for untrained input values.
A solution is to simply present the network with an array of input values that should result in the network outputting 0.
I'm relatively new to Matlab ANN Toolbox. I am training the NN with pattern recognition and target matrix of 3x8670 containing 1s and 0s, using one hidden layer, 40 neurons and the rest with default settings. When I get the simulated output for new set of inputs, then the values are around 0 and 1. I then arrange them in descending order and choose a fixed number(which is known to me) out of 8670 observations to be 1 and rest to be zero.
Every time I run the program, the first row of the simulated output always has close to 100% accuracy and the following rows dont exhibit the same kind of accuracy.
Is there a logical explanation in general? I understand that answering this query conclusively might require the understanding of program and problem, but its made of of several functions to clearly explain. Can I make some changes in the training to get consistence output?
If you have any suggestions please share it with me.
Thanks,
Nishant
Your problem statement is not clear for me. For example, what you mean by: "I then arrange them in descending order and choose a fixed number ..."
As I understand, you did not get appropriate output from your NN as compared to the real target. I mean, your output from NN is difference than target. If so, there are different possibilities which should be considered:
How do you divide training/test/validation sets for training phase? The most division should be assigned to training (around 75%) and rest for test/validation.
How is your training data set? Can it support most scenarios as you expected? If your trained data set is not somewhat similar to your test data sets (e.g., you have some new records/samples in the test data set which had not (near) appear in the training phase, it explains as 'outlier' and NN cannot work efficiently with these types of samples, so you need clustering approach not NN classification approach), your results from NN is out-of-range and NN cannot provide ideal accuracy as you need. NN is good for those data set training, where there is no very difference between training and test data sets. Otherwise, NN is not appropriate.
Sometimes you have an appropriate training data set, but the problem is training itself. In this condition, you need other types of NN, because feed-forward NNs such as MLP cannot work with compacted and not well-separated regions of data very well. You need strong function approximation such as RBF and SVM.
My task is to classify time-series data with use of MATLAB and any neural-network framework.
Describing task more specifically:
Is is a problem from computer-vision field. Is is a scene boundary detection task.
Source data are 4 arrays of neighbouring frame histogram correlations from the videoflow.
Based on this data, we have to classify this timeseries with 2 classes:
"scene break"
"no scene break"
So network input is 4 double values for each source data entry, and output is one binary value. I am going to show example of src data below:
0.997894,0.999413,0.982098,0.992164
0.998964,0.999986,0.999127,0.982068
0.993807,0.998823,0.994008,0.994299
0.225917,0.000000,0.407494,0.400424
0.881150,0.999427,0.949031,0.994918
Problem is that pattern-recogition tools from Matlab Neural Toolbox (like patternnet) threat source data like independant entrues. But I have strong belief that results will be precise only if net take decision based on the history of previous correlations.
But I also did not manage to get valid response from reccurent nets which serve time series analysis (like delaynet and narxnet).
narxnet and delaynet return lousy result and it looks like these types of networks not supposed to solve classification tasks. I am not insert any code here while it is allmost totally autogenerated with use of Matlab Neural Toolbox GUI.
I would apprecite any help. Especially, some advice which tool fits better for accomplishing my task.
I am not sure how difficult to classify this problem.
Given your sample, 4 input and 1 output feed-forward neural network is sufficient.
If you insist on using historical inputs, you simply pre-process your input d, such that
Your new input D(t) (a vector at time t) is composed of d(t) is a 1x4 vector at time t; d(t-1) is 1x4 vector at time t-1;... and d(t-k) is a 1x4 vector at time t-k.
If t-k <0, just treat it as '0'.
So you have a 1x(4(k+1)) vector as input, and 1 output.
Similar as Dan mentioned, you need to find a good k.
Speaking of the weights, I think additional pre-processing like windowing method on the input is not necessary, since neural network would be trained to assign weights to each input dimension.
It sounds a bit messy, since the neural network would consider each input dimension independently. That means you lose the information as four neighboring correlations.
One possible solution is the pre-processing extracts the neighborhood features, e.g. using mean and std as two features representative for the originals.
I have created a neural network and the performance is good. By using nprtool, we are allow to test the network with an input data and target data. Here is my question, what is the purpose of testing a neural network with target data provided? Isn't it testing should not hav e target data so that we can know how well can the trained neural network perform without target data is given? Hope someone will respond to this, thanks =)
I'm not familiar with nprtool, but I suspect it would give the input data to your neural network, and then compare your NN's output data with the target data (and compute some kind of success rate based on that).
So your NN will never see the target data, it's just used to measure the performance.
It's like the "teacher's edition" of the exercise books in school. The student (i.e. the NN) doesn't have the solutions, but her/his answers will be compared against them by the teacher (i.e. nprtool). (Okay, the teacher probably/hopefully knows the subject, but you get the idea.)
The "target" data t is the desired y of y=net(x) used as example to train the network.
What nprtool do is to divide the training set into three groups: the training set, the validation set and the test set.
The first one is used to actually update the network.
The second one is used to determine the performances of the net (note: this set is NOT used in any way to update the network): as the NN "learns" the error (as difference between the t and net(x)) over the validation set decreases. The trend will eventually stop or even reverse: this phenomena is called "overfitting", which means the NN is now chasing the training set, "memorizing" it at the cost of the ability to generalize (meaning: to perform well with unseen data). So the purpose of this validation set is to determine when to stop the training before the NN starts overfitting. This should answer your question.
Finally third set is for external testing, to leave you a set of data untouched by the training procedure.
Even though the total data set [training, validation and testing] are inputs to the training algorithm, the testing data is in no way used to design (i.e., train and validate) the net
total = design + test
design = train + validate
The training data is used to estimate weights and biases
The validation data is used to monitor the design performance on nontraining data. REGARDLESS OF THE PERFORMANCE ON TRAINING DATA, if validation performance degrades continuously for 6 (default) epochs, training is terminated (VALIDATION STOPPING).
This mitigates the dreaded phenomenon of OVERTRAINING AN OVERFIT NET where performance on nontraining data degrades even if the training set performance is improving.
An overfit net has more unknown weights and biases than training equations, thereby allowing an infinite number of solutions. A simple example of overfitting with two unknowns but only one equation:
KNOWN: a, b, c
FIND: unique x1 and x2
USING: a * x1 + b * x2 = c
Hope this helps.
Greg