All Questions
31 questions
22
votes
2
answers
24k
views
Convolutional neural network for time series? [closed]
I would like to know if there exists a code to train a convolutional neural net to do time-series classification.
I have seen some recent papers (http://www.fer.unizg.hr/_download/repository/KDI-...
- 391
9
votes
1
answer
149
views
Is there a ML or DL tool that can learn to detect periodically occurring patterns in a one dimensional time series?
I am trying to create a tool that labels refrigerator temperature readings. A reading is taken every 5 minutes, and its label identifies whether of not it was taken while the refrigerator was ...
- 91
8
votes
2
answers
16k
views
Classification in time series: SVMs, Neural Networks, Random Forests or non parametric models
My dataset is made of a label, $y_{t}$, which is the dependent variable, and about 20 columns of independent numeric variables, $X_{t}$, $t=1,2,...,T$.
These samples are time series and my goal is to ...
- 637
7
votes
2
answers
156
views
How to set up a DL classification model so that it selects from an ever changing menu
The question is edited for clarity after tchainzzz's comments about meta-learning.
Let's say we have 10,000 pet pictures and 10,000 kids. Each kid is presented with 10 randomly picked pet pictures at ...
- 205
3
votes
1
answer
2k
views
Which neural network architecture for time series classification?
I have time series consisting of 15 points of time, each containing around 15 values/features. Each time series is one sample, I have thousands of samples. Possible output is either 0 or 1, so binary ...
- 71
2
votes
2
answers
1k
views
neural networks - Inputting a time series to a classification NN
I have a simple ANN that does the job of classification between two labels-:
Sick
Healthy
What I want to do is that input patient data ie. heart rate(ECG), EEG, etc which will be in the form of a ...
- 1,143
2
votes
2
answers
1k
views
How to handle missing data in timeseries classification?
I'm using 16-channel, 400-Hz, standardized EEG data to train CNN-LSTM for seizure classification. The data contains periods of no recordings (flatlines) - spanning anywhere from seconds to minutes - ...
2
votes
1
answer
617
views
Time series classification using 1D-CNN with different-length input
I work on one-dimensional (1-D) time series classification using 1D-CNN. But the length of the time series data is variant, e.g., from 80 to 120. So it's hard to specify the size of input layer of CNN ...
- 21
2
votes
1
answer
2k
views
Multivariate Time Series Classification/Regression
Background, I'm predicting stock price change direction (either up or down) with about 200 predictors. All of them are time series data. We have about 1500 days as training/validation data.
My ...
- 121
2
votes
1
answer
27
views
How can I classify time-series given a predictor for each of them?
Say that I have two time-series and a predictor for each of them. I would like to build a classifer that given a window of future (and unseen) samples returns which series is more likely to have ...
2
votes
0
answers
480
views
How can I make sure that an LDA implementation works?
I am currently experimenting with neural nets for classification of on-line handwritten data (hence: not pixels, but time series data). To do so, I use several toolkits (internal development of my ...
- 1,767
1
vote
2
answers
612
views
Time Series classification problem (how to format data?)
I am working on a project where a physical test over time is conducted to decide whether an object is diagnosed as class $A$ or class $B$. Typically these tests can take around 2.5-3 hours and so each ...
- 49
1
vote
1
answer
2k
views
CNN architecture for 1D time series classification
I would like to use a CNN in order to classify signal data consisting of min. 500 data points into 3 categories. What kind of architecture and design considerations do I need to take into account and ...
- 63
1
vote
0
answers
28
views
Preprocessing and model selection strategies
I am working on a fault detection problem where each sample is a time series labeled with a specific type of fault. I am using a CNN model and a validation set for hyperparameter tuning. Currently, I ...
- 77
1
vote
0
answers
13
views
Why does GAP at the end of FCN for MTSC work?
I have a binary MTSC (Multivariate Time Series Classification) problem where i train a CNN, namely a FCN (or Fully Convolutional Network) to predict class 0 or class 1 based on a multivariate time ...
- 11
1
vote
0
answers
44
views
Time series normalization
I'm not an expert yet in the field and I have some questions. I have some data of birds and drones taken from a radar. I want to create a classifier that differentiates them. At first I'm trying and ...
- 11
1
vote
0
answers
82
views
Variable Length Input: How should variable-length input data be handled during the testing stage?
I have data that is sequential. Here, I am showing a toy example of my data in the following image:
I need to input the data into the model as groups of samples based on the class duration. To ...
- 161
1
vote
0
answers
711
views
What is the recommended way of normalizing time-series data for neural networks?
Introduction
Let me begin by describing the dataset and the application that I'm currently working with. I am working on a time series binary classification problem in Keras. My current approach uses ...
1
vote
0
answers
264
views
Softmax Classifier gives weird confusion matrix
I'm currently working on a problem of binary classification in keras and have decided to use the softmax function as the activation function for my final classification layer. My current network is as ...
1
vote
0
answers
141
views
Account previous features for classification with neural networks
My data for online testing looks like this:
\begin{array} {|r|r|r|r|r}
\hline
&feature~1 &feature2 &feature~3 &label(Yes~or~No)\\
\hline
three~steps~before &4 &3 &42 &...
- 11
1
vote
0
answers
82
views
What kind of neural network can I use for time series classification (preferably in MATLAB)?
I am pretty new to neural networks and am trying to figure out what network to use for my particular kind of data. I have a time series (frequency vs time) of radar returns. The radar either picks up ...
- 11
1
vote
0
answers
360
views
time series classification of an event either happening or not happening using machine learning techniques
I have sensor data that I would like to use to classify whether an an event (giving birth) is about to occur within (2-4hrs) in an animal based on various metrics collected by the sensor(activity ...
- 11
0
votes
1
answer
39
views
Deep learning classification with multiple temporal data
I'm working on a project to predict the category of music segments in an audio file (represented in pianoroll format with an additional column for the corresponding class). Each row represents the ...
- 1
0
votes
1
answer
230
views
Input Tensor Shape for CNN Binary Classification of Time Series Data
I want to predict whether a machine will fail based on the most recent set of measurements taken by on-board sensors. I have several dozen machines, each with a sensor that takes a measurement at ...
- 21
0
votes
1
answer
811
views
TIme series features using tsfresh - vector similarity
I have a dataset where there exists 16 different classes. Each class has about 400 rows of 12 attributes(iowait, read_bytes, write_bytes, etc). Using tsfresh, I have generated about 45 features(...
- 41
0
votes
1
answer
134
views
Time Series Classification?
I have univariate time series data for 70 subjects sampled at 1000 Hz. When graphing the single subject plots, time is on the x-axis and amplitude (arbitarty unit) on the y-axis. When looking at the ...
- 25
0
votes
1
answer
81
views
Using an image of data instead of the data when modeling
If we have many sequence data and corresponding plots, is there any reason we should use the image rather than the original data for deep learning?
Personally, I don't see any advantage of using CNN ...
- 177
0
votes
0
answers
19
views
How does highly imbalanced test data in certain splits of k-fold time-series cross-validation affect model performance?
I am working on a time-series classification (TSC) problem using k-fold time-series cross-validation (TSCV) to evaluate the performance of my models. My training data for each split is fairly balanced,...
0
votes
0
answers
11
views
Classification based on frequency decomposition of timeseries
I'm working on a classification problem where the dataset comprises a quote-unquote frequency profile from a timeseries. My dataset looks like this:
...
0
votes
0
answers
100
views
Model architecture approaches for event prediction at different timestamps
I would like to model a user event outcome (currently its binary).
the data I have is aggregated user activity and static user data.
here is an example of what the data looks like for clarity:
...
- 101
0
votes
1
answer
78
views
What neural network to use to classify time series data?
I am a self-learning machine learning, so this question may be a bit trivial. I've been reading "Python Machine Learning" by Sebastian Raschka and Vahid Mirjalili; however, I am still uncertain what ...
- 101