Questions tagged [lstm]
LSTM stands for Long Short-Term Memory. When we use this term most of the time we refer to a recurrent neural network or a block (part) of a bigger network.
1,174
questions
0
votes
0
answers
10
views
Lack of Variability in Predictions from Multivariate LSTM Model
I've been working on a multivariate LSTM model for time series forecasting, but I'm encountering an issue where the predicted output doesn't exhibit enough variability. The predictions tend to be too ...
- 101
1
vote
0
answers
30
views
Is it possible to determine the probability of each time sample to belong a certain class using gaussian distribution with Recurrent Neural Networks?
I'm trying to train a deep learning model that predicts the probability of each time sample in a two-component time series . In this case, I want the target tensor (Y) to be a probability value for ...
- 11
1
vote
1
answer
21
views
Out-of-Range Target Variable in Sequence-based Machine Learning Model
I'm encountering a scaling issue in a machine learning project. I'm predicting a target variable from an input sequence (and doing this for many). However, I've encountered a challenge where the ...
- 11
0
votes
0
answers
13
views
Is it possible to use leftovers data (warehouse stocks data) to create sales forecasting?
For example, i have sales data by categories
| Date | GE | VIC |
| -- | -- | --|
|03.01.2022 |2|7|
|10.01.2022 |30 |12|
|17.01.2022 |15 |5|
|24.01.2022 |57 |8|
|.....|...|...|
|28.08.2023 |16 |2|
And ...
- 1
0
votes
1
answer
23
views
LSTM For Predicting Vector Sequences
I am attempting to construct a Keras model that intakes a sequence of vectors and outputs the most likely next vector in the sequence. I have followed a few tutorials, but nothing is quite seeming to ...
- 13
0
votes
0
answers
12
views
Tensorflow RNN - implementing recursive layer
I am dealing with a regression problem, for which I wanted to try to use a recurrent neural network. The general setting is that I have to predict a continuous quantity starting from the evolution, in ...
0
votes
0
answers
12
views
Training multi-variate LSTM model with sample observations with differenet mean values
I am developing an LSTM model to predict the force-deformation response for wind turbine blades. I have generated the training data from a high-fidelity model for wind speeds ranging from 3m/s to 25m/...
0
votes
0
answers
17
views
Cant pass input_shape to LSTM layer in Keras
I have a numpy array X_train of shape (number of samples, timestep , number of features) =...
- 221
0
votes
1
answer
31
views
Converting a Standard LSTM RNN over to a Transformer Model
I am looking for some advice on converting my existing CNN/LSTM RNN over to a Transformer type model. This regression model takes a sliding window size of 240 rows with 33 features. It aims to ...
- 111
0
votes
0
answers
20
views
What is the reason behind high frequency output from LSTM model?
Following is the time history response of my input features, which has relatively low frequency component
My LSTM network architecture is as follows:
...
0
votes
0
answers
10
views
Stable test in online time series forecasting problem
I have a Time Series Forecasting problem. You can think of it as predicting the daily closing prices of Apple stocks. My data is divided into 4-day segments, and the forecasting is based on predicting ...
- 63
0
votes
0
answers
14
views
Confusion about LSTM network with multiple LSTM units
An LSTM layer can have multiple LSTM units ( LSTM memory cells each with input/update, output, and forget gates). In this question, we have 2 LSTM layers each with two cells, and according to the ...
- 221
0
votes
0
answers
16
views
Can a multivariate MIMO LSTM forecaster learn the relationships between the multiple feature outputs?
Question: Can a multivariate MIMO LSTM learn the relationships between the multiple feature outputs?
This question arose when I decided to modify a multivariate (Multiple Input - Single Output, MISO) ...
0
votes
0
answers
18
views
Predicting quanting sold using Time series data
I am struggling with a time series dataset comprising 12 features, including quantity sold and weather data, totaling approximately 1800 values. My goal has been to forecast future values, quantity ...
- 1
0
votes
0
answers
15
views
Validation loss hump in LSTM
I'm using PyTorch to fit an LSTM to a binary time series dataset which has about 300 time series of about 20 items. I am using 15% of the time series as a validation set. I then have an MLP on top of ...
- 101
0
votes
0
answers
11
views
Meaning of mean squared error in multistep prediction
In multistep prediction with LSTM(keras), say we had this kind of result:
target = [[1,2,3] ,[4,5,6] ]
predictions = [[1.1,2.2,3.3] , [4.4,5.5,6.6]]
When we choose mean_squared_error as the loss ...
- 23
0
votes
0
answers
19
views
LSTM - How can I predict the status an hour before in advance?
I’m very beginner, I’m trying to design a prediction model for forecasting the status one hour ahead.I have 150 sample data, each consisting of of 24 hours of time-series data with multiple features (...
2
votes
1
answer
120
views
Role of stateful parameter vs shuffle parameter in LSTM keras
I'm trying to make prediction on a multivariate time series using LSTM. I know stateful=True in keras LSTM means state(hidden) of each sequence, in a batch, at index i - is passed to the next batch, ...
- 23
0
votes
0
answers
42
views
Custom Loss Function Returns Graph Execution Error: Can not squeeze dim[0], expected a dimension of 1, got 32
I have built a loss function which adds time and frequency weighted averages and variances to the MSE:
...
1
vote
1
answer
30
views
LSTM Training Interpretation
I have an LSTM and have the following chart showing training validation performance by epoch:
Could someone explain? How can my validation performance be better than my training performance in ...
- 143
0
votes
0
answers
57
views
Value Error: One of the dimensions in the output is <= 0 due to downsampling in conv1d_9
i am trying to implement classification model on my dataset, which has 3 columns and 651 rows
Displacement Time Labels
0.000245879 0.01 Undamage
0.001954869 0.02 Damage
0.006545664 0.03 Undamage
0....
- 1
0
votes
0
answers
17
views
RNN model for predicting sequences based on sequences of different lengths with Keras
I have data that are sequences of repeated values of different lengths. The value is categorical and can take values from 1 to 184. I used padded with 0 and masking:
...
- 1
0
votes
0
answers
24
views
Forecasting using LSTM Model
I have a dataset with 12 variables (x1, x2, ..., x12) and one target variable (y). Is it possible to perform a forecasting for the target variable (y) over a certain period of time ahead without ...
- 1
0
votes
1
answer
36
views
Identification of inexactly-recurring material in time series stream
I am working on a personal project involving the analysis of a stream of audio data and the identification of (non-verbatim) repeated subsequences.
My research on time series has so far lead me to ...
- 101
0
votes
0
answers
7
views
Creating insights from Battery monitoring parameters (State-of-charge, battery cell voltages, temperature, etc.) to use with AI or model based
So, in a new role currently and I decided to pursue the health monitoring and impending failures of batteries (Lithium Iron Phosphate, Lithium Ion and a few lead-acid as well) but having never done it ...
1
vote
0
answers
23
views
Validation Accuracy incorrect when multiple outputs are in the dense layer!
I have a set-up a parallel LSTM architecture with two LSTM layers and one Dense layer producing several outputs which are converted into a probability with a sigmoid function on the dense layer. For ...
- 11
0
votes
2
answers
84
views
How to add multiple embeddings (layers) to LSTM layer
The similar question was asked before here https://stackoverflow.com/questions/52627739/how-to-merge-numerical-and-embedding-sequential-models-to-treat-categories-in-rn/52629902#...
0
votes
0
answers
12
views
Timeseries timestep changes in deployment
Im working with spatiotemporal data and I'm wondering if I train my model on a timeseries with a certain timestep, do I need to have the same timestep when I deploy the model and make predictions?
...
- 1
0
votes
0
answers
37
views
heavy underfitting of keras LSTM regression
I moved the question from stackoverflow to here.
I used keras LSTM to do the standard regression project of ...
- 127
0
votes
0
answers
24
views
ROC curve for multiclassification - results sound not correct
I'm working on a multiclassification task using LSTM algorithm, i generated my roc curve plots but they give scores like 1 , 0.99, 0.97 however i have an accuracy of 0.97, Precision 0.65, Sensitivity/...
- 11
0
votes
0
answers
12
views
Does the LSTM model recognize the spatial and temporal pattern in my input data?
I have data from a row of sensors that sense a body passing over it. The data is of the shape (NS, NT) where NS is the total number of sensors and NT is the total number of time steps. The sensor data ...
0
votes
0
answers
35
views
Flickr8k+PyTorch, CNN+LSTM predicts always same words during model testing
I'm a beginner in Machine Learning and I'm working with the Flickr8k dataset (it contains ~8000 images, every image has 5 captions: ~40000 pairs).
I splitted the dataset in training (70%) and ...
- 1
0
votes
0
answers
82
views
Get graph execution error for BiLSTM and LSTM on keras
I want to use BiLSTM mode for text classification tasks. I use a data generator to get already batched and embedded files that have been split into 64 different files(for training) and 4 files each ...
- 1
2
votes
0
answers
45
views
LSTM output capped at a maximum
I am using a LSTM built using to forecast a single-value (solar irradiance) by using weather data as my input.
When predicting my validation test, I get a weird results as it looks like all my ...
- 273
0
votes
0
answers
15
views
Need help designing conv-lstm in TensorFlow for longitudinal disease prediction
I am currently trying to develop a conv-lstm to predict disease progression in eye photos of patients. I have a folder of images with a total of 263 images of 144 different patients. I also have an ...
- 1
0
votes
0
answers
17
views
Is this problem a time series regression or seq2seq regression or some other type of problem?
I measure sequences of 3 parameters in my system. 2 are independent and the 3rd dependent. Let's call the independent ones $x$ and $y$, and the dependent one $z$. They are each measured once per hour ...
0
votes
0
answers
45
views
Modelling LSTM Autoencoder for anomaly detection with multiple Time Series
I'm currently working on LSTM autoencoder for anomaly detection. My main problem is I have multiple time series - each individual time series corresponds to a different customer, detailing their sales ...
- 1
0
votes
1
answer
40
views
How did Andrej Karpathy make the LSTM output byte values for sampling Shakespeare?
I'm wondering how continuous output values of deep learning networks are converted to byte values or other discrete values for that sake.
For example here: In his famous article The Unreasonable ...
- 103
0
votes
0
answers
16
views
Sending rolling statistics to RNN
I'm curious if anyone has seen cases where sending rolling statistics such as mean, median, min, max, standard deviation, skewness, kurtosis, etc. have been helpful for model accuracy?
If so please ...
- 41
0
votes
0
answers
18
views
0
votes
0
answers
16
views
Matrix time-series Forecasting with LSTM
I have the following time-series data:
...
2
votes
3
answers
119
views
What is the most optimal machine learning model/algorithm to create a hangman solver?
Want to create a hangman solver, So what is the best ml algorithm (lstm,reinforcement learning, or etc) to use?
Do suggest any other optimal technique if you know?
- 21
0
votes
0
answers
18
views
Detecting Anomalies Using LSTMs
I'm studying this article. The authors used a two-step approach to detect anomalies. First, they used an LSTM to learn the normal behavior of the data. Then, they used the dynamic error thresholds to ...
- 113
0
votes
1
answer
79
views
Constructing an LSTM autoencoder for variable-lentgth sequences
I would like to construct an LSTM autoencoder model for sequence anomaly detection where the sequences can be varying in length. I understand based on this answer that padding and masking can be used ...
- 123
0
votes
0
answers
35
views
ML model to predict CPU utilization of a server given x amount of tasks
I have comprehensive data points of what the CPU utilization of a server is when x amount of jobs are running, let's say the server is using 40% CPU util time=x and there are 4 jobs running.
The ...
0
votes
0
answers
17
views
Finding a template RNN for time series analysis
I would like to create a RNN, that uses one (A) or several time series (with the same length, A, B, C...) as an input and creates another time series (Z) as an output from that .
Basically all time ...
- 1
0
votes
0
answers
30
views
LSTM multivariate forecasting
I'm currently working on timeseries forecasting in pairs where one timeseries is suspected to cause the other timeseries. I also have the forecast of the causing timeseries and I use them to predict ...
- 1
0
votes
0
answers
33
views
How to grid search LSTM manually using TimeSeriesSplit while preserve/load best_models?
This is a self-written LSTM tuner class.
...
- 1
0
votes
0
answers
7
views
Dataframe shape for multi-point prediction using LSTM & time-serise data
Can I ask a very simple question about LSTM time-series data prediction?
I have 1258 data points which are Amazon's historical stock price by day unit.
And I build the below data structure in order to ...
- 113
0
votes
0
answers
17
views
multi label multi class classification problem
I am trying to solve a aspect based sentiment analysis problem.
I am considering to devise a NN but am not sure if it is doable the way I am doing.
here is how I structure it.
I have a training set of ...
- 1