Newest 'lstm' Questions

0 votes

0 answers

13 views

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)...

Marianne A.K

11

asked 20 hours ago

0 votes

0 answers

7 views

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 ...

deep-learning lstm rnn sequence-to-sequence gru

Juliet Soujbel

1

asked yesterday

0 votes

0 answers

10 views

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 ...

keras tensorflow dataset lstm sequence

teoML

121

asked May 23 at 11:02

1 vote

1 answer

33 views

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 ...

keras lstm anomaly-detection

farhanrbn

13

asked May 23 at 3:56

0 votes

0 answers

19 views

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 ...

keras tensorflow cnn lstm

Camillo

1

asked May 19 at 8:44

0 votes

0 answers

16 views

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 ...

python scikit-learn lstm pytorch

Ali_d

101

asked May 18 at 9:03

1 vote

1 answer

21 views

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. ...

lstm rnn pytorch

Alp Evr

13

asked May 17 at 20:51

0 votes

0 answers

23 views

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 ...

classification time-series lstm multiclass-classification multilabel-classification

Coinman

13

asked May 16 at 9:52

0 votes

0 answers

27 views

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 ...

lstm predictive-modeling convolutional-neural-network autoencoder

warriorforce

11

asked May 15 at 16:59

1 vote

1 answer

20 views

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,...,...

neural-network keras time-series lstm rnn

Mich55

13

asked May 10 at 15:08

1 vote

0 answers

9 views

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 ...

lstm reinforcement-learning attention-mechanism

Esmaeel Mohammadi

11

asked May 10 at 12:30

0 votes

0 answers

10 views

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 ...

neural-network time-series lstm supervised-learning normalization

Հայկ Ավետիսյան

1

asked May 9 at 11:45

0 votes

0 answers

23 views

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 ...

time-series lstm rnn loss-function

user1246462

101

asked May 9 at 5:37

0 votes

0 answers

12 views

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: ...

python keras pandas lstm

Kosmylo

101

asked May 6 at 10:51

0 votes

0 answers

26 views

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: ...

deep-learning lstm features

bill

101

asked May 5 at 14:10

0 votes

0 answers

15 views

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 ...

python classification time-series cnn lstm

ILKetchup

29

asked May 5 at 7:39

0 votes

0 answers

16 views

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 '...

python deep-learning neural-network lstm

FABI99

1

asked May 3 at 19:26

0 votes

0 answers

29 views

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....

machine-learning deep-learning neural-network time-series lstm

learnlifelong

23

asked Apr 30 at 5:55

0 votes

0 answers

10 views

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 (...

python tensorflow lstm

Fancq

11

asked Apr 29 at 14:13

0 votes

0 answers

11 views

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 ...

machine-learning deep-learning time-series lstm

Monzer

1

asked Apr 29 at 6:49

0 votes

0 answers

11 views

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: ...

keras tensorflow cnn lstm data-science-model

Ulwan M Rasyid

1

asked Apr 28 at 10:27

0 votes

0 answers

5 views

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 ...

deep-learning nlp lstm text-classification sentiment-analysis

nem0

9

asked Apr 27 at 10:37

1 vote

1 answer

20 views

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 ...

machine-learning deep-learning time-series lstm forecasting

Canovich

21

asked Apr 27 at 1:52

0 votes

0 answers

7 views

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. ...

neural-network time-series lstm forecasting

SJa

131

asked Apr 25 at 16:22

0 votes

0 answers

13 views

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 ...

keras lstm

Corvus

101

asked Apr 25 at 8:18

0 votes

1 answer

26 views

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 ...

lstm rnn training anomaly-detection autoencoder

learnlifelong

23

asked Apr 23 at 19:10

0 votes

0 answers

12 views

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 ...

lstm rnn autoencoder

learnlifelong

23

asked Apr 23 at 0:16

0 votes

0 answers

6 views

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 ...

time-series lstm pytorch autoencoder

David Waterworth

372

asked Apr 21 at 23:13

1 vote

0 answers

36 views

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 ...

python keras tensorflow lstm

Fancq

11

asked Apr 20 at 13:15

0 votes

0 answers

14 views

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 ...

machine-learning deep-learning time-series lstm anomaly-detection

learnlifelong

23

asked Apr 19 at 4:11

0 votes

0 answers

29 views

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 ...

neural-network keras tensorflow lstm text-generation

lazarea

259

asked Apr 16 at 21:45

1 vote

0 answers

14 views

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 ...

nlp lstm rnn text-generation ngrams

Tejax

11

asked Apr 16 at 16:43

0 votes

0 answers

16 views

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 ...

lstm rnn convolutional-neural-network multiclass-classification sequence

Kevin

143

asked Apr 16 at 13:51

0 votes

0 answers

10 views

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 ...

lstm rnn training

Caspertijmen1

121

asked Apr 12 at 20:23

0 votes

1 answer

13 views

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 ...

time-series lstm training

Caspertijmen1

121

asked Apr 12 at 19:13

1 vote

0 answers

20 views

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 ...

keras time-series lstm

User771

11

asked Apr 11 at 16:41

0 votes

0 answers

13 views

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 ...

neural-network classification time-series lstm rnn

Debayan Datta

11

asked Apr 11 at 3:09

1 vote

1 answer

25 views

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, ...

machine-learning deep-learning nlp lstm tfidf

mewbie

53

asked Apr 9 at 12:27

1 vote

1 answer

32 views

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 ...

machine-learning deep-learning nlp lstm rnn

mewbie

53

asked Apr 8 at 15:06

2 votes

0 answers

12 views

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 ...

machine-learning python neural-network tensorflow lstm

James Chong

21

asked Apr 7 at 18:41

0 votes

0 answers

18 views

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:/...

machine-learning lstm sentiment-analysis

Bluetail

101

asked Apr 6 at 13:01

2 votes

0 answers

21 views

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 ...

lstm rnn multiclass-classification transformer sequence

Kevin

143

asked Apr 5 at 14:38

0 votes

1 answer

18 views

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 ...

machine-learning neural-network time-series lstm forecasting

Zaratruta

127

asked Apr 4 at 3:31

0 votes

0 answers

15 views

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 ...

machine-learning deep-learning time-series lstm forecasting

Zaratruta

127

asked Apr 4 at 1:19

0 votes

0 answers

20 views

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. ...

r lstm text-mining text

Mustafa Kamal

1

asked Apr 2 at 21:35

0 votes

0 answers

16 views

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 ...

keras time-series lstm

Alejo Dev

101

asked Apr 1 at 18:34

0 votes

0 answers

13 views

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 ...

deep-learning tensorflow lstm loss-function mse

Hanna

1

asked Mar 30 at 9:10

0 votes

0 answers

9 views

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 ...

time-series lstm training

Stupid_Intern

157

asked Mar 26 at 3:59

0 votes

0 answers

36 views

best trial always found at first optuna trial

I am using optuna as part of the pytorch forecasting library. I executed the following code: ...

lstm pytorch forecasting hyperparameter-tuning

Ruut

101

asked Mar 25 at 21:37

0 votes

0 answers

9 views

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 ...

tensorflow time-series lstm rnn pytorch

Atheros

1

asked Mar 23 at 12:03

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