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Questions tagged [lstm]

A Long Short Term Memory (LSTM) is a neural network architecture that contains recurrent NN blocks that can remember a value for an arbitrary length of time.

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Distribution function of LSTM and MLP for different cases [duplicate]

I asked this question(which can be considered as Case1), MLP is inferior to LSTM in TEC forecast. It can be observed that the TEC curve forecasted by LSTM is closely fit for the observed TEC curve ...
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Approximation function for MLP and LSTM

I have total of 6300 samples, 5800 of which are training data, and 500 of which are testing data. We compare the performance of LSTM and multilayer perceptron (MLP) with one hidden layer in terms of ...
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Improving Robustness of LSTM Model for Stock Price Prediction

I am currently working on a Long Short-Term Memory (LSTM) model for predicting stock prices. My model takes into account the fact that there are non-trading minutes with no data. I have also ...
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How to train with convLSTM2D on variable input shape?

I am classifying time series of 72x72 images in 4 filters (just like RGB). Things work well ...
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How to improve prediction quality of LSTM model

I am trying to train an LSTM-based model in MATLAB to predict 365 next values given 365 previous values of a time series. Input shape is (1000, 365) and output shape is (1000, 365) i.e. there are 1000 ...
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Test loss immediately goes up on LSTM

I'm trying to create an LSTM that predicts the sixth sports match for team A based on a sequence of 5 previous matches. My data is set up in a structure like this. Team A game 1 vs random team, team B ...
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Is there any quick and easy "good" way to test if an LSTM is working (in the basic sense)?

I'm definitely a beginner and trying to get a handle on things so I apologise in advance for possible slowness of understanding :) Recently, I came across a problem where given some sequence of ...
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LSTM input vector

Trying to understand how LSTM works, i have found this article: https://colah.github.io/posts/2015-08-Understanding-LSTMs/ Consider we have a matrix with $N$ rows that contains time series (input ...
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Question on a paper which talks about stacking several fully connected layers of 2 neural networks

So to preface, my knowledge on neural networks is very limited, and I've had a very difficult time trying to comprehend the details of this paper. My background is in maths, and I've created a ...
Rowan Harley's user avatar
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Adjusted R2 for LSTM

Background: I am working on a problem, where I am making predictions for a time-series data. I am considering two approaches: Use LSTM, predict n samples using recursive strategy (suggested e.g. in ...
Michał Panek's user avatar
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How does autoregressive training help limit compounding errors at inference?

I'm having a little trouble justifying something in my head and was hoping someone could provide some intuition? I understand for LSTM models or models that maintain some state about a sequence that ...
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Backpropagation in LSTM network [closed]

as we have Vanishing Gradient in Vanilla RNN and LSTM is the solution , according to some sources LSTM has Vanishing Gradient too but it doesnt cause any problem in the context of LSTM network cause ...
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How to use LSTM, TFT, or other RNN with time series of different lengths

I have a dataset of financial transactions per day with the volume of the transaction and the price of the transaction. For each day I want to calculate the volume weighted average price at the end of ...
Mathematician....'s user avatar
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How to prepare sequential word data for many-to-many LSTM?

I want to build a many-to-many LSTM and I need some guidance on how to prepare the data that I have for such a model. My data frame consists of subject_ID (dance couple), Time (in s, time step in a ...
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Why is validation accuracy fluctuating between two extremes?

I am using a LSTM to make a multi-label classifier. However the problem is, the validation accuracy fluctuates between 0.7 and 0.3. I have about 4367 samples (80-20 validation split) and 240 sample ...
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Multivariate Time Series dataset preparation

I am a bit confused with the time series dataset preparation. From the internet, I saw all examples which used tree-based models, had input features and target defined as: ...
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Predicting quantiy 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, where data is recorded on a daily basis. My goal has ...
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forecast model has shifted result (lag = -1)

I have a problem with the LSTM forecast model; it predicts the current value instead of the next value. I have checked the data alignment, and it is correct. Try with: different scallers and unscale ...
Eman Saleh's user avatar
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Can this be considered overfitting?

I have been trying to use the LSTM model for a monthly time series with a length of 404 (384 for training and 20 for test). I created 4 pairs of training/validation sets, trained different models, and ...
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Training Set, Validation Set and Test Set for Time Series

I have monthly time series data on stock prices from January 1990 to August 2023. I tend to use walk-forward validation to compare the forecasting performance of ARIMA and LSTM models on this time ...
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Timesteps with ignoring certain values

I'm currently working on a lstm which use as data energy produced from solar panels collect every 5 minutes. We currently use a time step of 4 and we use "the 20 minutes" before to predict ...
Nicolas's user avatar
2 votes
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Gradient Clipping of Vanilla RNNs vs LSTMs

I am doing an online course that states that the reason we use LSTMs and similar variations of vanilla RNNs is because of the vanishing/exploding gradients problems with vanilla RNNs. However, an ...
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Assistance with LSTM Keras model for predicting heart rate from velocity

Good Morning, Currently, I'm new to Kears and neural networks in general. I'm working through Deep Learning In R With Keras with a 'capstone' project in mind, but I'm struggling to understand how to ...
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Getting a Many to One LSTM/MLP to overfit

I have a dataset of 20 thousand horses. For each horse, I have its 10 last historical races (finishing time, position, track name, distance etc. for 41 features) and am trying to predict its finishing ...
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Strength of LSTM for text prediction

I am trying to understand the working of LSTM by considering an application. Given below is the application of LSTM for spam prediction a well known dataset. ...
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Does LSTM keep its state between subsequent samples in the training process?

I'm not sure if I understood well how LSTM works. I know that the LSTM layers keep an internal state. But I'm not sure if this state is maintained between subsequent samples during the training ...
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Test results with trained LSTM model

I have 10,000 data for training of LSTM model. 10,000 dataset is extracted from database for the years before 2020. 60% of data is used for training, 20% for validation and 20% for test. Test data is ...
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Predicting new timeseries based on related timeseries?

Let's say I have multiple timeseries, representing different features, all of length n, and I want to predict a new timeseries which represents another feature, without any past history for that ...
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I want to use LSTM to predict the arrival time of navires (boats/vessels)

The data that I have contain the time of arrival of the vessel (TA) and also the time spent to berth (ETB) for every vessel during the last year. I want to predict the ETB using LSTM in a deep hybrid ...
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Can LSTMs/RNNs' Final Hidden States Serve as Effective Sequence Embeddings for Document or Sentence Analysis?

Can the final hidden state of LSTMs/RNNs be utilized as a sequence embedding for documents or sentences? These embeddings could then be applied to tasks like classification or clustering to leverage ...
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Does an LSTM make sense in this context?

I have a continious variable I wish to predict, for which I have sensory data on a fixed time interval t, denoted as y_t. In addition, I have a set of features which are consistent across time, called ...
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Are there Companies that publish stock prediction source code [closed]

Now the best way to describe this is as follows: For example Epic Games and Unity and Blender have the source code of their engines available for the public to read. Reading company code can be very ...
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Prior estimation in Dynamic (sequence to sequence) Variational Autoencoders (DVAE) with LSTMs

I am trying to implement a sequence-to-sequence variational autoencoder that consists of two parallel sequence encoders. One of the encoders is based on a standard normal prior as in the vanilla vae (...
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Input shapes for multiclass LTSM classification model in tensorflow

I am building a classifier for 7 classes where my array shapes are as follows: ...
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ValueError while fitting a neural networks model on Consumer Complaints data

I am trying to build a keras tensorflow neural network model. I have never built a NN model prior so facing challenges with some very basic error. I Was able to build the model using following code. I ...
Rohit Jain's user avatar
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Derivation of Truncated Derivatives in Hochreiter's original LSTM Paper

I was trying to understand the derivation of the truncated derivatives in Hochreiter's origin LSTM paper. Spent a bunch of time without avail. I suppose I am missing a way to map my intuition of ...
saiftyfirst's user avatar
2 votes
1 answer
111 views

How to handle hyperparameter tuning for LSTM with early stopping?

I am looking for advice on the best practice to determine hyperparameters for my LSTM model. I have time series data that I have divided into train and test sets. I was planning to use an expanding ...
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Time series prediction problem formatted correctly for LSTM neural networks?

I am new to machine learning, I am trying to find a way to predict voltage waveforms into the future. I have seen examples that successfully predict sinusoids or continuous voltage data based on ...
Maximiliami's user avatar
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RMSE of Training data is lower compared to test dataset

First of all, this is not a case of Overfitting. The task is to forecast Temp using univariate Single Step forecasting. I have trained the LSTM model with on jena climate dataset(Dataset https://...
Hemant Yadav's user avatar
1 vote
1 answer
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Is the count regression model valid in this use case?

I was doing a project where my target variable is a count variable. So naturally my mind went to Poisson regression, but upon further inspection, any identical set of my explanatory variables can ...
Rocky Balboa's user avatar
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RNN/LSTM networks on spectrograms underfitting massively - is the CNN encoder a prerequisite?

I am prototyping a pipeline on the FSDD dataset (audio/10-class classification); the audio data are loaded with librosa, 0-padded/trimmed to 0.5 sec (4000-dimensioned numpy vectors) each and converted ...
Nikos H.'s user avatar
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LSTM validation accuracy fluctuating [duplicate]

I am using LSTM to model time series data. My target variable is categorical so I am using one-hot encoding. The goal is to predict the target class based on the given time. My dataset spans over ...
user2585933's user avatar
4 votes
2 answers
200 views

Model only exceeds baseline in a certain fine-tuning condition

I am training an vanilla 5-layers LSTM. My task is trying to compare two models between without (baseline) and with the additional features (compared model). However, I found out that the compared ...
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Why is the accuracy of a Decision Tree decreasing whereas the accuracy of an LSTM is increasing when adding augmented data?

I am using sklearn's DecisionTreeClassifier and LSTMs (Keras) for time series classification. To increase the accuracy and robustness of the models I augmented the ...
Unistack's user avatar
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313 views

LSTM accuracy not increasing more than 68%

I am a newbie in the field of deep learning. I am trying to use LSTM with time series data with more than 300,000 datapoints. The target variable is a class with 7 unique values. I used one-hot ...
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Relative changes instead absolute values in LSTM training

I am reading some papers about glucose time series prediction and I have noticed that some of them propose LSTM models that use relative changes between two measures. For example, if $$ glucose(t)=60, ...
renton01's user avatar
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27 views

Much better results when standardizing features to train LSTMs

I have a data set of time series. Each time series represents trajectories of the same path taken. So, the time series captures acceleration in $x$, $y$ and $z$ direction, respectively for the ...
Invader's user avatar
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Model Architecture Multi-Agents

My question is based on a previews post: How to train LSTM model on multiple time series data? . The task is basically the same. However, my question builds on that: What does the model architecture ...
ChrisChross's user avatar
1 vote
1 answer
194 views

Time series forecasting for dataset spanning a week

I am a newbie in time series data forecasting. I have a week long data and the counts represent arrivals per 5 mins period. A part of the dataset is shown below. ...
Hania's user avatar
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Why LSTM predicts very poorly with loss curves showing no indication of overfitting or underfitting?

I am training a LSTM model for load demand prediction with: Training Data: 18288 samples with 9 features Validation Data: 0.05% of Training Data (about 915 samples) Data Scaling: MinMax Scaler(0,1)---&...
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