Questions tagged [long-short-term-memory]

For questions related to the long-short term memory (LSTM), which refers to a recurrent neural network architecture that uses LSTM units. The first LSTM unit was proposed in 1997 by Sepp Hochreiter and Jürgen Schmidhuber in the paper "Long-Short Term Memory".

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13 views

LSTM performance strictly decreases with sequence length input

I'm working on an event binary classification problem. More specifically, for a given event E I know some info about the event itself just before it's supposed to happen, encoded in an embedding ...
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15 views

LSTM - What kind of data should contain every dimension of input LSTM matrix, where does specific dimension points to?

I am a beginner and I have a hard time understanding inputs and outputs of LSTM. So from the begining, I am trying to create multivariate input&output LSTM for time series forecasting. Thankfully ...
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46 views

Is my intuition about RNN wrong?

Until today, my intuition about RNN (LSTM/GRU) was that this is some kind of NN that can remember previous inputs. Consider a task where you need to predict 0 if the previous input was 1. For example: ...
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Are there any resources about using RL with RNN to produce Open AI Five-type of AI?

I want to make a minimal working version of Open AI Five. It seems it uses PPO with LSTM, but I don't know how to implement the actual code, and couldn't find any online tutorials for it. Are there ...
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56 views

What is the time complexity for testing a stacked LSTM model?

In the data preparation phase, we have to divide the dataset into two parts: the training dataset and the test dataset. I have seen this post regarding the time complexity for training a model. ...
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50 views

Is my dataset a time series dataset? and should I use an LSTM?

I have a dataset where I am recording temperature after every 4milliseconds till 500 and another feature "conductivity value". The length of the dataset is around a 1000 rows. I need to find ...
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1answer
19 views

Is a recurrent layer same as LSTM or single-layered LSTM?

In MLP, there are neurons that form a layer. Each hidden layer gives a vector of number that is the output of that layer. In CNN, there are kernels that form a convolutional layer. Each layer gives ...
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71 views

Which ML algorithm is the best for predict the next PRNG generated numbers?

I have a homework. The task is to decide, if the PRNG generated lottery is attackable/crackable or not. Details: Lottery: There is a lottery game where you have to choose 8 numbers between 1-20 for ...
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68 views

Why is there tanh(x)*sigmoid(x) in a LSTM cell?

CONTEXT I was wondering why there are sigmoid and tanh activation functions in an LSTM cell. My intuition was based on the flow of tanh(x)*sigmoid(x) and the ...
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85 views

Why does the number of input tokens to an LSTM have an impact on the convergence of Integrated Gradients?

Background I am computing the attribution scores for a simple LSTM model using Integrated Gradients. This method defines the contribution of a feature to a model prediction by integrating over the ...
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How sensitive are LSTM's to random zero values in its target feature when training?

I have worked with lstm's in the past, specifically for time series forecasting. However, the target feature in these time series were relatively "stable". With the loosely defined "...
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10 views

Categorical Location based Time Series data Prediction using LSTMs

I have some time series data with ActivityType as location shared below. Each CaseID has 6 unique values with different ...
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29 views

Is this a good implementation of this LSTM architecture?

I had been looking at some OCR problems and came across this presentation. I implemented it. In the presentation, there is the LSTM-Stack (diagram and algorithm, slide 32): Here is a visualization of ...
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How Long Can BPTT Truncated?

I wanted to ask what is, in general, the maximum value (the order of magnitude) of the number of time steps I can back-propagate in the past using TBTT (Truncated Backpropagation Through Time) in an ...
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17 views

Boundedness of cell states in MC-LSTM (mass-conserving LSTM)

I'm currently reading Hoedt et al's paper on mass-conserving LSTM. In the corollary it is stated that "[T]he memory cells, $c_k^\tau$, are bounded by the sum of mass inputs $\sum_{t=1}^\tau x^t+...
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Machine learning with raw data alone / or raw data with its statistics

My question is very general and it does not originate from a specific problem. Let's assume that, through experience, we have learned that some statistical property of a set of data is important in ...
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771 views

Is LSTM a subcategory of RNN?

Is the LSTM-Architecture a subcategory of RNNs? Or are they totally different? Literature doesn't seem to be unitary on this. This figure appears to explain the models to be alternatives, but I ...
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27 views

Do LSTM in tensorflow work sequentially or in parallel

I have a basic understanding how a cell and a layer of an LSTM works. However, I get confused by what "number of units" (as termed in tensorflow) exactly means. A unit is, as far as I ...
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1answer
47 views

Reinforcing Learning when action has no effect on the environment

I am trying to get my head around a problem where the action by the agent can not change the environment. Without going into details, my problem is about error correction in an stochastic environment. ...
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31 views

What type of neural network do you need if you want to detect an action or dynamic pattern instead of a static pattern?

Let's say that you want to detect if a man is running, walking, or dancing instead of just detecting a man still. What type of neural networks will you use for this purpose?
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41 views

What's the best way to feed stories to a neural network?

I'm trying to train a model that would generate stories. I have a dataset of 2000 stories prepared. They are tokenized and one-hot encoded. I can't load them all at once as a one big dataset, because ...
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The results are not correct when predicting the future for a very long period of time with LSTM

I am currently using LSTM to try to predict future data in AirPassengers.csv. This is current code op my Colab (sorry for the comments are Japanese) https://colab.research.google.com/drive/...
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19 views

Time series forecasting with some challenges

I'm attempting to devise a strategy to make time series forecasts based on costs accumulated over time. My dataset contains about 7500 time-series sequences (call it an instance for now), each having ...
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26 views

What is the reason for a training loss that drops but validation that NEVER does

I've been working on learning about NLP via a beginners competition on Kaggle. I first trained a model with an embedding layer and then a simple linear layer. I actually got way better than a flip of ...
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35 views

LSTM and CNN - feature engineering and order for time series classification

My questions are related to multivariate time series classification, hence it may differ from forecasting problems. I can have either variable (entire history of the series) or fixed time steps (...
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15 views

LSTM predictions are one time step lagging

My problem involves electricity prediction (time-series problem) for 1-hour ahead. I am using LSTM to forecast. Length of Dataset: 1 year at one-hour interval Input: Outdoor Temperature (Ot), ...
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34 views

How Long Short Term Memory (LSTM) work for time series classification?

I first got the concept of LSTM on how it works word to word prediction etc. However, I want to know how it work with the time-series classification. For example I have the follwing data (see image ...
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18 views

How will actual labels be matched with predicted labels when LSTM discards data even from current time stamp input data?

I read the tutorial of LSTM from here. However, I have certain doubts that I need to address. Since we use true labels and do not remove anything from the original data, then how is it possible for ...
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27 views

Time series forecasting for multiple objects with common features

I know the title of this question may raise an eyebrow, but I can't find the technical terms to define or investigate the actual problem. To demonstrate my problem with a simple hypothetical scenario: ...
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1answer
54 views

Using a Neural Network (LSTM) to approve/reject word-type sequences

I would like to train an LSTM neural network to either "approve" or "reject" a string based on the word-type sequence. For instance: "Mike's Airplane" would output "...
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1answer
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14 views

End-to-end learning using LSTM-AE

I want to use prediction models like LSTM-AE to predict time-series data. The feature that the neural network should learn is in frequency between 40-60Hz. So, in order to learn the feature more ...
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1answer
29 views

How can I address missing values for LSTM?

I'm a student and writing my first paper for submission on conference. I have a question there is a dataset below. this is temporal-spatial dataset. ...
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1answer
54 views

Why doesn't the LSTM model improve the time-series forecasting significantly with respect to the MLP model?

I have recently started learning time series forecasting. I have a dataset of the weekly payment history of 10k clients over 1 year, and I want to predict the future 5 payments for a test set of 1k ...
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26 views

Keras MLP performing better than Transformers

I'm working on a comparative study using some models in a sentiment analysis task: MLPs and LSTMs with and without the use of word embeddings (GloVe and Word2Vec) and two Transformer-based models (...
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10 views

Feature selection by Simple regression vs finite impulse response (FIR) method (on TIME Series analysis)

We are working on prediction one company production estimation and the main field of works is like stock market prediction(Time series analysis and process data). So I have some comment on using ...
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36 views

LSTM Recursive Forecast

I am confused about the way the LSTM networks work when forecasting with a horizon that is not finite, but I'm rather searching for a prediction in whatever time in future. In physical terms, I would ...
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17 views

Seq2Seq Models not used for NLP - input to the Decoder?

I am looking into Seq2Seq models but using it to make multi-step predictions of factory data and I am getting a little confused with the inputs to the Decoder model. Correct me if I am wrong, but the ...
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10 views

LSTM Forecast Evolution

I have a confusion about the way the LSTM networks work when forecasting with an horizon that is not finite but I'm rather searching for a prediction in whatever time in future. In physical terms I ...
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54 views

What do RNN, LSTM, and GRU layers do in Tensorflow?

I have gone through some theoretical introductions of RNN and LSTM, which do not contain any code, and they describe in fair detail what the cells do, how they apply operations like forget, sigmoid, ...
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1answer
26 views

Changing a CNN-LSTM image captioning architecture to use BiLSTMs

Currently I'm dealing with an assignment that made us implement the network mentioned in this paper. The network has an architecture similar to this: As you can see it uses a Unidirectional RNN (in ...
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1answer
38 views

What is it about sigmoid activations in particular that allows for the keeping and forgetting of past information from different time scales?

My understanding is that normal recurrent neural networks (RNNs) are not good at keeping past information from different time scales. Furthermore, my understanding is that Gated RNNs, such as Long ...
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21 views

Neural Networks different architectures but similar training curves

I have a base neural network architecture for (3D) image sequences classification, made of conv layers followed by a LSTM and dense layers. I have 3 similar architectures : 3 Conv -> 1 LSTM -> ...
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What does it mean when predicted results are constant values?

I'm practicing with some data with a LSTM neural nets to come up with predicted data, comparing with actual data. I generated an image to show what I came up with. The blue line is actual data, and ...
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46 views

Forecasting of spatio-temporal event data

I’m currently working on my dissertation which is centred around forecasting social conflict events. I’m using data from GDELT (Global Database of Events, Tone, and Language) to develop my forecasting ...
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Is my dataset unlearnable, or is my LSTM model not smart enough?

I have time-series data obtained from a video. The data is composed of bitrate and corresponding label pairs for each timestamp: The distribution over the first 30 seconds is as follows: I have ...
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1answer
87 views

Rescaling time-series data with very spiky pattern for training data in LSTM network

I am working with some time-series hydrology data. Our goal is to forecast the time series forward, meaning predicting the data 1 month, 3 months ,6 months into the future. The data itself(image below)...
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19 views

Understanding metrics, understanding my LSTM results

I'm trying to learn about forecasting time-series methods, my first approach to achieve it is using LSTM. Lets suppose I have my data well processed, and I have my data time-series correctly. In that ...
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1answer
50 views

What kind of neural network should I build to classify each instance of a time series sequence?

Let's say I have the time-series dataset below-left. I would like to train a model in such a way that, if I feed the model with an input like the test sequence below, it should be able to classify ...
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1answer
50 views

Recommended Time serie forecasting model for Fibonacci levels classification

I have a set of time series data which gives me fibonacci levels and the duration at which the value is at this level. Data structure to look like: ...

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