I would like to ask you if there is any automated method for calculating the order of ARIMA(p,d,q) model for any type of a time series data, in MATLAB.
This will make the forecasting model more accurate and will also save me some time. I would appreciate if anybody could help me.
I'm not sure if that's what you are looking for, but maybe this post on Matlab File Exchange?
http://www.mathworks.com/matlabcentral/fileexchange/25611-arfima-simulations
Related
I am aiming to build a global/general forecasting model (don't know what's the proper terminology) using deep learning. The idea behind this is to create a model trained on several time series that will allow me to obtain forecasts for the time series used during training and for others not used during training.
I think this is a mixed problem, classification-regression. If I am correct and this is a mixed problem then: How should I do it?
Should I work only with the variable/s I aim to forecast (using lagged observations). I mean passing through the model in question (I think it could be a CNN-LSTM or a LSTMGC model) only the objective variable/s. If this is an option I would like to be explained or pointed to an explanation of how this model works and how should the data for the training be structured.
Or should I pass to the model the categorical variables as well. Being in the case of "product sales" variables like region, type of product the categorical ones and sales or amount of selled items the variables to forecast. Regarding to this I have the same doubt as before. (In addition think this could be a model easier to interpret)
Will be of a great help if anyone could point to or explain to me if there is any methodology on how to solve this kind of problems using deep learning. Aspects like what are the most typical ANN structures to solve this kind of problem, how should the data be prepared and how should the model be trained.
Thanks in advance for all the help.
Original question posted in Artificial Intelligence Stack.
I am new to Neural Network and I dont know what exactly to search on google for solution,here is my problem ,if you kindly please let me know what I am looking for,
So I am working on a project where,it will have many contributors over time,and each contributor will write a new line on excel file and then run the code to train dataset,
if want to ask is that ,is there a way to save a checkpoint so each time the code don't have to train the whole dataset and just continue to train the new entries instead of starting from zero.
Please let me know what exactly I should google.
Kind regards
This is, as you guessed, extremely common and usually referred to as "fine-tuning". In your case, since the dataset barely changes between training runs, you can expect the model to be very similar, so you could initialize your weights to the weights of the previous best model and retrain for only a few epochs, likely with a small learning rate.
People usually do fine-tuning starting from a network trained on an entirely different dataset, so it's likely that you will find that use-case rather than yours, but it will work even better if you keep a very similar dataset.
"Continual learning without forgetting"
In MATLAB R2014b there is a new function, pca(), that performs PCA that can handle missing data. In the documentation it says that it performs pca with the "alternating least squares" algorithm in order to estimate the missing values.
I would like to know if there are any practical references in how to apply PCA with this algorithm without the use of the function, or if there is a good reference on als. The reason is, there is no such function in Octave that can handle missing data and so I would like to code it myself.
Thanks for all your help. I went through the references and was able to find their matlab code on the als algorithm from two of the references. For anybody wondering, the source code can be found in these two links:
1) http://research.ics.aalto.fi/bayes/software/index.shtml
2) https://www.cs.nyu.edu/~roweis/code.html
I'm trying to uncover the transition parameters of data of a hidden Markov Model using MATLAB. Using the built in hmmtrain function, I can estimate the parameters quite well (I already know what they are supposed to be as it's simulated data for now), but they are always off by a little. So what I want to do is construct a confidence interval for these parameters.
Now, I've searched the web for this quite a bit and it seems as if this is not easy to do. The 'easiest' solution appears to be bootstrapping, but I personally don't know how to go about and implement something like that in MATLAB, for a model as complex as a HMM. Does anyone have any references I could look at that could help me write a script for this, or does anyone know of a package out there that already has this built in?
Edit:
The main issue I have is that I wouldn't know how a bootstrap method would tackle the problem of HMM. Isn't the whole idea of HMM that you get a specific sequence from which you learn about the parameters? Wouldn't cutting it up for the purpose of bootstrapping automatically create some errors?
I have a 2D graph that I have created of frequency vs. amplitude as a way to characterize a signal. I am trying to figure out how to write script that takes the WAV I have already imported into MATLAB, and figure out the peaks, the troughs, and the distance between each one. Can someone please help me with some general guidelines, not a complete code, for a script that will help me do this?
There are several submissions on the file exchange for this. For instance this one, seems applicable (I haven't tested it myself, but it looks good).
Try to search a bit on the file exchange and you will likely find something you can use.
You can also have a look at this question, and this one.