I have been working on a multivariate time series problem. The dataset has at least 40 different factors. I tried to select only the appropriate features before training the model. I came across a paper called "A Multiattention-Based Supervised Feature Selection Method for Multivariate Time Series. The link to the paper:"https://www.hindawi.com/journals/cin/2021/6911192/
The paper looks promising however I could not find the the implementation of it. I would like to know if anyone has come across a similar paper and knows how to implement the architecture in the paper?
If not, I want to know alternate methods to find only the appropriate features for my multivariate time series before training the model.
Related
I am working on a long-term time series (wind speed) forecasting model with different deep learning algorithms. I am using MLP, CNN, and LSTM. I have several questions, and I would appreciate it if you could answer them.
-Do I have to do any preprocessing for seasonality for these deep learning models?
Why is my R-square so bad and sometimes negative?
When I plot the predicted model on the train or test, it is obvious that the model is not good since it is like a straight line and does not capture the trend. However, my evaluation parameters are really good. For example, the RMSE, MAE, and MAPE are 0.77, 0.67, and 0.1, respectively. So is it enough to just report these parameters as many articles have?
And the last one, is it possible to use the proposed model for different datasets? Is it reasonable if I use another city wind speed dataset with a different pattern and trend on this model? Because I have seen many articles that have done it and my models are not working on different datasets.
I am a student and currently studying deep learning by myself. Here I would like to ask for clarification regarding the transfer learning.
For example MobileNetv2 (https://keras.io/api/applications/mobilenet/#mobilenetv2-function), if the weights parameter is set to None, then I am not doing transfer learning as the weights are random initialized. If I would like to do transfer learning, then I should set the weights parameter to imagenet. Is this concept correct?
Clarification and explanation regarding deep learning
Yes, when you initialize the weights with random values, you are just using the architecture and training the model from scratch. The goal of transfer learning is to use the previously gained knowledge by another trained model to get better results or to use less computational resources.
There are different ways to use transfer learning:
You can freeze the learned weights of the base model and replace the last layer of the model base on your problem and just train the last layer
You can start with the learned weights and fine-tune them (let them change in the learning process). Many people do that because sometimes it makes the training faster and gives better results because the weights already contain so much information.
You can use the first layers to extract basic features like colors, edges, circles... and add your desired layers after them. In this way, you can use your resources to learn high-level features.
There are more cases, but I hope it could give you an idea.
I am working on a project about time-series forecasting using LSTMs layers. The dataset used for training and testing the model was collected among 443 persons which worn a sensor that samples a physical variable ( 1 variable/measure) every 5 minutes, for each patient there are around 5000 records/readings.
Although, I can train and test my model under different scenarios, I am troubled finding information about how to apply transfer learning in such an architecture. I mean, I understand I can use inductive transfer-learning by copying the matrix-weights from the general model onto a secondary model (unknown person), then after I can re-train this model with specific data and evaluate the result.
But I would like to know if somebody knows other ways to apply transfer-learning on this type of architecture or where to find information about it since there aren't many scientific papers talking about it, mostly they talk about NLP and other type of application but time series?
Cheers X )
This is essentially a question on fundamentals, and whether or not there is a more efficient way to achieve what I am looking for. I have built a working fluid dynamics calculator in Excel to find the flow rates required for a target pressure loss, the optimisation is handled using Solver but it's very clunky and not user friendly.
I'm trying to replicate the function in Octave since it's widely used here, but I am a complete beginner; I'm probably missing something obvious. I can easily enter all of the math for a single iteration via a series of functions, but my excel file required using the 'Solver' macro, and I'm unsure how to efficiently replicate this in Octave.
I am aware that linprog (in matlab) and glpk (octave) can be used to solve systems of linear equations.
I have a series of nested equations which are all dependant on a single matrix, Q (flow rates at various locations). Many other inputs are required, but they either remain constant throughout calculation (e.g. system geometry) or are dictated by Q (e.g. Reynolds number and loss coefficients). In trying to simplify my problem I have settled on two steps:
Write code to solve my problem, input: Q matrix, output: pressure loss matrix
Create a loop that iterates different Q matrices until some conditions for the pressure loss matrix are met.
I don't think it will be practical to get my expressions into the form of A*x = B (in order to use glpk) given the complexity. In excel, I can point solver at a Q value that drives a multitude of equations that impact pressure loss, and it will find the value I need to achieve a target. How can I most efficiently replicate this functionality in Octave?
First off all Solver is not a macro. Pretty far from.
So, you're going to replicate a comprehensive "What-If" Analysis Plug-in -- so complex in fact, that Microsoft chose to contract a 3rd Party company of experts to develop the tool and provide support for it (successfully based on the 1.2 Billion copies they've distributed).
And you're going to this an inferior coding language that you're a complete beginner with? Cool. I'd like to see this!
Cool. Here's a checklist of Solver's features, so you don't miss anything:
Good Luck!
More Information:
Wikipedia : Solver
Office.com : Define and Solve a Problem by using Solver
Frontline: Official Solver Page: http://solver.com
AppSource.Microsoft.com : Solver (with Video)
Frontline:L Solver International Manazine
I am fairly new to Deep Learning and get quite overwhelmed by the many different Nets and their field of application. Thus, I want to know if there is some kind of overview which kind of different networks exist, what there key-features are and what kind of purpose they have.
For example I know abut LeNet, ConvNet, AlexNet - and somehow they are the same but still differ?
There are basically two types of neural networks, supervised and unsupervised learning. Both need a training set to "learn". Imagine training set as a massive book where you can learn specific information. In supervised learning, the book is supplied with answer key but without the solution manual, in contrast, unsupervised learning comes without answer key or solution manual. But the goal is the same, which is that to find patterns between the questions and answers (supervised learning) and questions (unsupervised learning).
Now we have differentiate between those two, we can go into the models. Let's discuss about supervised learning, which basically has 3 main models:
artificial neural network (ANN)
convolutional neural network (CNN)
recurrent neural network (RNN)
ANN is the simplest of all three. I believe that you have understand it, so we can move forward to CNN.
Basically in CNN all you have to do is to convolve our input with feature detectors. Feature detectors are matrices which have the dimension of (row,column,depth(number of feature detectors). The goal of convolving our input is to extract informations related to spatial data. Let's say you want to distinguish between cats and dogs. Cats have whiskers but dogs does not. Cats also have different eyes than dogs and so on. But the downside is, the more convolution layers will result in slower computation time. To mitigate that, we do some kind of processing called pooling or downsampling. Basically, this reduce the size of feature detectors while minimizing lost features or information. Then the next step would be flattening or squashing all those 3d matrix into (n,1) dimension so you can input it into ANN. Then the next step is self explanatory, which is normal ANN. Because CNN is inherently able to detect certain features, it mostly(maybe always) used for classification, for example image classification, time series classification, or maybe even video classification. For a crash course in CNN, check out this video by Siraj Raval. He's my favourite youtuber of all time!
Arguably the most sophisticated of all three, RNN is bestly described as neural networks that have "memory" by introducing "loops" within them which allow information to persist. Why is this important? As you are reading this, your brain use previous memory to comprehend all of this information. You don't seem to rethink everything from scratch again and this is what traditional neural networks do, which is to forget everything and re-learn again. But native RNN aren't effective so when people talk about RNN they mostly refer to LSTM which stands for Long Short-Term Memory. If that seems confusing to you, Cristopher Olah will give you in depth explanation in a very simple way. I advice you to check out his link for complete understanding about how RNN, especially LSTM variant
As for unsupervised learning, I'm so sorry that I haven't got the time to learn them, so this is the best I can do. Good luck and have fun!
They are the same type of Networks. Convolutional Neural Networks. The problem with the overview is that as soon as you post something it is already outdated. Most of the networks you describe are already old, even though they are only a few years old.
Nevertheless you can take a look at the networks supplied by caffe (https://github.com/BVLC/caffe/tree/master/models).
In my personal view the most important concepts in deep Learning are recurrent networks (https://keras.io/layers/recurrent/), residual connections, inception blocks (see https://arxiv.org/abs/1602.07261). The rest are largely theoretical concepts, which would not fit in a stack overflow answer.