All Questions
Tagged with neural-network time-series
135
questions
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25
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Is it legit to normalize time series with respect to the x-axis?
I have a data set consisting of multivariate time series, e.g. a batch of my data has the shape (batch_size, timesteps, number_input_features) and I want to train a neural network on it to predict ...
- 13
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1
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30
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How to explain missing dates to a model?
I have this dataset that I'm trying to train a neural network on.
The problem is that since weekend dates are not available, I am not confident in whether the model is able to account for that. ...
- 101
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9
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Positional Encoding for FFNN?
Here is my problem: I have input [x1,..,xt,n1,..,nt,1,2,...,t] where there is a missing timestep xi, and I use neighboring time series (found with KNN) n1,...,nt to add more features, as well as time ...
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42
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How do I give weight to recent time points when predicting another closeby time point?
I am building a normal feed-forward neural network to predict the value of a masked time point using regression, e.g. I have values for x at times 1, 2, and 4, and I want to predict its value at time ...
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20
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Do categorical embeddings leak data in time series?
I am a bit confused on this matter, I can't find any resources that touch on the following but my logic says that embeddings do introduce data leakage in time series:
Considering a temporal dataset ...
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36
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deep learning for stock prediction
I am learning deep learning . Right now I am using MNIST data set, which contains tens of thousands of scanned images of handwritten digits, together with their correct classifications.
My question ...
- 101
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13
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Temporal mismatch
I am building a predictive model to determine risk for a disease over the course of a hospital stay. I am using medical records from a hospital electronic medical record database. The predictions are ...
- 131
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19
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What is the advantage of positional encoding over using additional features?
Popular models such as the transformer model use positional encoding on existing feature dimensions. Why is this preferred over adding more features to the feature dimension of the tensor which can ...
- 11
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53
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Weighting training instances by time in machine learning models
I am training a neural network based on data whose relevance I think diminishes based on how far each instance is in the past. I've had a look and one way to do this it seems is to 'weight' training ...
- 11
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1
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63
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Which ML algorithm is suitable for a dataset that has seasonality and trend?
I have a small dataset from 2006 to 2023, I would like to predict monthly sales for the next year. This is my data:
I already tried Prophet and NeuralProphet, but unfortunately they don't work well ...
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1
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1
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37
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Time series predictions with multiple series
I have a dataframe shaped something like this
patient_id
admission_id
admit_date
diagnoses
1
1
2125-10-18
[1,2]
1
2
2125-10-26
[1,2,3]
1
3
2125-11-30
[1,2,3,4,5]
...
4
7
2130-06-23
[2,3,4,7,9.......
- 11
1
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23
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What machine learning technique can help generate spectrum line profiles?
I'm trying to work with Calcium-K line profiles from the Sun.
Image for reference. Please ignore the labels on the image and note that my profiles are not in image format (more info below).
I have ...
0
votes
1
answer
265
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Correlation between multiple time series
For research, we put some test samples through a physical process for a certain period of time and make measurements. The general structure of the data we collect is as follows:
...
0
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1
answer
38
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Time series prediction problem with other parameters
My data looks like this: time series of average house prices per day in 100 cities (maybe more) over a two-year period, each with a set of characteristic variables that do not change over time, such ...
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1
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0
answers
44
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LSTM for classification
I am new to neural networks and I want to use LSTM to classify the on/off state of devices based on power values. In my training dataset, I have power values, device one (0,1), and device 2 (0,1). 0 ...
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