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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.

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Regression sequence output loss function

I am fairly new to deep learning, and I have the following task. Based on an audio sequence of shape (200, 1024), I have to predict two sequences of shape (200, 1) of continuous values (for e.g 0.5687)...
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RNN to model DNA sequencing classification

I have a DNA sequence dataset each mapped to a certain class. e,g TCAGCCGAGAGCTCATCGATCGTACGT 2 ATGCAGTGCATCGATCGATCGTAGAAC 3 Where the number after the sequence specifies the type of protein this ...
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Train an LSTM on separate sequences of different lengths

My case is the following: I want to train a sequential classifier to recognize what action is being performed given sensors observations.My data consists of 10 executions of an assembling task for 10 ...
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is it good to have 100% accuracy on validation?

i'm still new in machine learning. currently i'm creating an anomaly detection for flight data. it is a multivariate time series data that include timestamp, latitude, longitude, velocity and altitude ...
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Keras/Tensorflow Error: Specified a list with shape [3,1] from a tensor with shape [32,1]

I have been experimenting with keras/tensorflow to build up my confidence and am currently trying to build a LSTM model that forecast the price of a stock based on the price of the stock in the ...
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How to train ML model for time series data

I am trying to build a machine learning model in python. I used pytorch and sklearn to make the model. My model is a bit ...
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What is the purpose of Sequence Length parameter in RNN (specifically on PyTorch)?

I am trying to understand RNN. I got a good sense of how it works on theory. But then on PyTorch you have two extra dimensions to your input data: batch size (number of batches) and sequence length. ...
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Multi-Label time-series classification with LSTM: large performance decrease for longer periods

I have daily data on event occurences, so for each day I have a vector like [1, 0, 1] indicating that on this day event one and three occured, but event two did not occur. I want to train a model to ...
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What model and attributes would be good for this data?

I have the following set of data like in the picture, with 366 Temperature values for one year. The first set of data would be for training and the second one for test. I would like to detect the ...
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RNN/LSTM timeseries, with fixed attributes per run

I have a multivariate time series of weather date: temperature, humidity and wind strength ($x_{c,t},y_{c,t},z_{c,t}$ respectively). I have this data for a dozen different cities ($c\in {c_1,c_2,...,...
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Custom Simulator for Deep Reinforcement Learning

I am trying to develop a control method for a specific process in industry. I have a time-series of data for the process and want to develop a prediction model base on attention mechanism to estimate ...
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Proper iteration over time series data for LSTM neural network

I’m using the supervised learning method with an LSTM network to predict forex prices. To achieve this I’m using deeplearning4j library but I doubt several points of my implementation. I turned off ...
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PyTorch: LSTM training loss not decreasing; starting at very high loss

I am training an LSTM to give counts of the number of items in buckets. There are 252 buckets. However, I am running into an issue with very large MSELoss that does not decrease in training (meaning ...
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NaNs in predictions with LSTM

I have an LSTM model that I have trained and tested it with a dataset. Now I want to test it to an other dataset and I use the following snippet: ...
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LSTM for binary classification using multiple attributes

I haven't used neural networks for many years, so excuse my ignorance. I was wondering what is the most appropriate way to train a LSTM model based on my dataset. I have 3 attributes as follows: ...
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Time series classification using multiples multivariate multi-length timeseries

0 I would like to develop a time series classification algorithm to classify use a of parachute. My data consist of multiple recording files (around 5min at 100hz, length of the recording vary) with a ...
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Which LSTM Training Strategy Performs better?

I would like to use LSTM for predicting multiple time series (Time series about sales per day in multiple countries. In parts, contradicting regional trends are present within the data. Sales is the '...
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Performance metrics for LSTM Autoencoder

I am building an LSTM Autoencoder (unsupervised model) to detect anomalies in a time series dataset. The input is telemetry data from routers and I want to detect anomalies in the throughout of router....
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Static and dynamic feature for hybrid modle

I would like to make a hybrid model in this way : I have a question about input data. My dynamic input is like [ [ [5],[4],[7] ],[ [7],[5],[7] ],...]. So shape is (...
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Problem with timestamp

I have a data set with 2 timestamps (1 hour and then 15 minutes). how can I standardize the timestamps as 15 minutes? is it a practical practice to add other 3 rows (each one is 15 minutes) with the ...
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Predicting value using LSTM

I'm currently learning about LSTM and want to make a prediction using an array as an input and have an output as a single value. I currently trying to do that by using this model: ...
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predictive effect in the classification made according to the comments in different fields

I want to do a classification through comments categorized in 4 areas(X,Y,Z,M). Categorizing the product as good or bad based on the comments in the fields X, Y, Z, M. How can I follow a path to see ...
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Removing seasonality in time series forecasting

In time series forecasting we are removing the "seasonal" component to fit models better and have better forecasting. But why? if I should give an extreme example: if I have a sin wave, I ...
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Can preprocessing in time-series data (e.g. deseasonaliation or detrending) helps create better forecasting model?

I am reading a paper that mentions the following. ...
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How to use LSTM for multiple individual but predicting at every timestep

My dataset consists of a large (1000s+) number of individuals, who may be considered independent of each other. Each individual has a timeseries of about 10-60 data points (each point being a vector ...
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Training data for anomaly detection using LSTM Autoencoder

I am building an time-series anomaly detection engine using LSTM autoencoder. I read this article where the author suggests to train the model on clean data only in response to a comment. However, in ...
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LSTM Autoencoders vs LSTM

I'm working on a time-series anomaly detection project. I have read that both LSTM Autoencoders and LSTM can do the job. Can someone please help me understand what are the advantages of each i.e. when ...
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LSTM Auto-encoder Implementation

I'm trying to implement an LSTM auto-encoder in PyTorch for time-series data (univariate and/or multi-variate). Initially, I assumed it would be fairly easy but I realised there are a few ...
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Lstm model predict only float between 0 and 1

i created a lstm model to predict number of crimes in months. For each month I predict the number of crimes for the next month. The targets are values ​​greater than 1 (normally) but the model ...
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One anomaly detection model for all industries

Background - I'm creating a time-series anomaly detection (TSAD) model for the wifi throughput. My customers are 2 banks, 5 retail stores, 4 universities, 6 hospitals. Currently, I have 2 options to ...
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Using LSTM for text generation keeps generating same word

I work on a simple text generation problem using a portion of the Shakespeare dataset that I decided to use LSTM for. I primarily used this tutorial for reference. However, as I ran the below code, I ...
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Does N-gram language model for text generation are more efficient than Neural Network language models?

I recently build an language model with N-gram model for text generation and for change I started exploring Neural Network for text generation. One thing I observed that the previous model results ...
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1D Sequence Classification using Circular Dilated Convolutional Neural Networks

I am working on a multiclass classification task on long 1D sequences. The sequence length may vary between $512$ and $512 \cdot 60$ timesteps, a slice of $100$ timesteps might look like this: What ...
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How to use teacher forcing in a LSTM

For my timeseries problem it seems obvious to use teacher forcing. For example in the case of predicting the new timestep in a real life scenario, I do have access to all the ground truths for all ...
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How to train-test split a timeseries?

I have a dataset consisting of multiple timeseries for multiple users. So per user I have multiple timesteps, a value to predict per timestep and a list of features per timestep. I am currently ...
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Time Series Forecast using LSTM and Neural Network

I am doing a univariate time series forecast using Keras. The first Image shows forecasts using a neural network and the second image shows forecasts using an LSTM model. The models have been trained ...
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How to use LSTM on Human Interface Data?

I have to classify gestures using LSTM or any other neural network approach. For every time step(row), I have 34 features that belong to a gesture. Like this, some gestures correspond to a number of ...
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NLP Basic input doubt

I actually have a basic doubt in NLP, When we consider traditional models like Decision trees, The feature column order is important, Like first column is fixed with some particular attribute. So If, ...
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NLP LSTM input basic doubt

I have a basic doubt with regards to conversion of text to numbers and feeding it to LSTM. I am aware of the different methods such as OneHot, CountVectorizer, TfIDF, Word2vec etc. My doubt is, If we ...
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Possible LSTM architectures and training methods to fill in missing trajectory data

Please see the picture below which represents a trajectory of something like a car, person, or boat moving in 2-dimensional x and y space, and their points in that space are plotted here: What if the ...
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How to increase the model accuracy and how to choose the number of epochs in a LSTM model from accuracy and loss curves?

I am doing a NLP sentiment analysis task using an LSTM model (which currently gives me a 50% test accuracy as compared to 84% of a Naive Bayes). It is a text corpus of movie reviews from here (https:/...
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1D Sequence Classification

Cross-post from https://stackoverflow.com/questions/71752744/1d-sequence-classification I am working with a long sequence (~60 000 timesteps) classification task with continuous input domain. The ...
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Dealing with inputs of different sizes in time series forecasting

I'm dealing with a task where I need to forecast the n-ith value of a target variable in a multivariate time series. But in this case we have two variables: -var1: Is my target variable that ...
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Multivariate time series forecasting and LSTM: When should I separate time series in different inputs

Let us suppose that I have a multivariate time series with two variables that vary together in time: var1 and var 2. And let us suppose that I want to forecast the n-ith value of var 2, by considering ...
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How to convert a string variable containing comments to a variable with integers to be used in neural networks?

I am working with data contains comment variable like imdb data. ...
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Using keras LSTM layer for time series prediction

I am trying to get an LSTM model to predict the next value of a time series given the previous n values, but I haven't been able. So I coded the simplest example I could think of, in order to ...
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weighted mse - weights as function of time

I am predicting timeseries data using LSTM (in tensorflow). Currently I am using MSE as my metric of choice. I would like to create my own custom Weighted MSE metric, such that the weights are a ...
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Can I train the model using previously saved model?

If I have trained an LSTM model and saved it and after few months if I have new data can I use the saved model to train it on new data? Why I am asking this is because say if I use the saved model to ...
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best trial always found at first optuna trial

I am using optuna as part of the pytorch forecasting library. I executed the following code: ...
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Dataset Preparation for LSTM (multiple variables)

I am struggling to conceptualize the correct way to prepare a timeseries dataset for LSTM training. My main concern is how do I train the network to 'remember' N previous steps. I have two possible ...
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