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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How can I train a LSTM with different time series of same process?
I have multiple time series dataset of the same process (e.g: sensor collecting humidity in a manufacturing process which last 2 hours) and would like to train a LSTM model to make forecast based on ...
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How to compare different forecasting models over different time horizon?
Developed multiple Models with AR, ARIMA, VAR; LSTM , SARIMA.
Now, the purpose is to find out which model performs best on a given use case with different time horizons.
The time series data is ...
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How to apply one-to-many LSTM using Keras?
I am finding it difficult to wrap my head around the one-to-many approach using Keras LSTM block.
I have 7 input parameters, using which I need to predict a sequence of length 650.
I referred to LSTM ...
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Question regarding multivariate LSTM model
I am currently working on a multivariate LSTM model to forecast stock prices and am getting confused about how this model works.
For univariate, it is straight forward. I have a dataset with only one ...
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Composite Input into Seq2Seq LSTM Network
Given that we have a seq2seq problem, where the input sequence is indeed multiple inputs and not only one as in traditional seq2seq problems. For example, in language translation, we usually give ...
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How does data shuffling work when LSTM is involved?
TIL that when using the LSTM layer, the states are remembered throughout the same batch. When using stateful LSTM, they can be even remembered outside of the batch.
The first realization gave me a ...
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Models for Long-Term Time-Series Forecasting and Pattern Recognition
I'm trying to find a solution for long-term electricity hourly prices forecasting. Explaining simply, I have some data from 2018 - 2021 containing Demand, Renewable Generation, Hydropower Generation, ...
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14 views
Making use of several time series in one LSTM model
I am working on a case where I want to do a multivariate and multi-step time series forecasting.
I have hourly data that measures temperature at approximately 500 different devices. (the devices have ...
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Time Series Forecasting with LSTMs in keras - convergence problem
I am trying to forecast a time series with multivariate input and multi output (multi step forecast). Since some of my input features are known for future time steps, wheras others are not, naturally ...
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7 views
FFNN vs. RNN for Regressing Physical Sensor Timeseries Data
I'm trying to build a network to regress data from one sensor to another. The target sensor is a scalar time series and the feature sensor can be either a scalar or vector time series. Both timeseries ...
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14 views
How to improve my deep LSTM model for time series?
I want to train a deep model for my time series power consumption dataset. I have created a model consist of CNN, BILSTM, Encoder-Decoder, and dense layers. here is my model:
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Backward LSTM in Pytorch
I'm in the process of rebuilding a network using PyTorch. The Keras implementation uses a LSTM module with the parameter go_backwards=true:
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Improve accuracy on LSTM - Multiclass Classification problem
Problem Description
I need to build a model which solves the following problem. I have a sequence (let's say size=n) of integers (arrivals) , which looks like this 0,0,1,5,2,...,4,8,6 , and I want to ...
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64 views
Please explain Transformer vs LSTM using a sequence prediction example
I don't understand the difference in mechanics of a transformer vs LSTM for a sequence prediction problem. Here is what I have gathered so far:
LSTM:
suppose we want to predict the remaining tokens ...
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7 views
Why does this LSTM example and the y shape output I did come out differently?
I did the same as the example, but the y_predicted.shape output is different.
is this example
https://www.youtube.com/watch?v=s3CnE2tqQdo&list=PL7fNr5pdVm7UGJHLK5q0tZ6NZJqxGAofU&index=21&...
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Why is sequence prediction always the objective in RNN and LSTM like algorithms
The title is pretty much my question. I haven't seen any literature yet that uses a different training objective. The goal is to find the hidden states eventually, then why is it that only 1 method is ...
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Is it always beneficial to use return_sequences=True for time series prediction with RNN?
I roughly understand what return_sequences=True does when being used for time series prediction with RNN (each RNN cell outputs its hidden state). Now my question ...
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Multi site/source and Multivariate time series data (with multi time step) input in LSTM for forecasting
I am trying to make a multisite multivariate LSTM forecasting model with Keras. I have a simple Multivariate data structure like 3 X variables and 1 target variable with time-step 10, so my input ...
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Why the LSTM on Keras does not work correctly when it is necessary to predict several steps forward
I used AirPassenger Dataset. And based on several previous values(for examples 20) I want to predict several(3 or 5) steps in future.
Like
X -> y
[10,20,30,....200]->[210,220,230]
[20,30,40,.......
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Grouped Time Series forecasting with scikit-hts
I am trying to forecast sales for multiple time series I took from kaggle's Store item demand forecasting challenge. It consists of a long format time series for 10 stores and 50 items resulting in ...
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2answers
63 views
LSTM model, poor performance
I have been working on a project on the demand for a product.
I am using data from 2016 to train the LSTM model.
The architecture is as follows:
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22 views
How to Inference With Keras Sequential Models (Text Classification)
I have the following LSTM model and I can't make inference with it:
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Conceptual question - is it correct to use categorical variables such as day, month, year as a fixed sequence input in LSTM?
I am working on a problem where I have to try to predict the dependent variable (continuous) every hour based on hourly temperature (the single continuous variable in predictor space), along with 4 ...
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LSTM or GRU for Time-series Multilabel classification
Univariate time series data with only one feature vector (e.g. 1x1300 as a time step), a superposition or sum of different signals, should be disaggregated or ...
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How to extract skills from job description using neural network
I am doing a project where I have to extract skills from Job Description.
I have attempted by cleaning data(not removing stopwords), applying POS tag, labelling sentences as skill/not_skill, trained ...
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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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Improving the accuracy of a Bidirectional LSTM model?
I have a model that I have spent the past few days trying to train and gradually improve. The data is of sequential nature, and I am trying to use an LSTM to classify the data as one of the three ...
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14 views
Not using an input feature during a model evaluation process that was used in the training process
Assuming I have a dataset that consists of three different types of data column: x, y and z. x and y are data retrieved from sensors, where z is inputted manually. The goal of the deep learning model ...
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LSTM Log Returns Producing Small Values
I am trying to predict log returns using an LSTM. My predictions are consistently much smaller than the actual values. I have tried using different scaling methodology and multiple different ...
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6 views
Validation Loss does not decrease in LSTM?
I am runnning LSTM for classification task, and my validation loss does not decrease. What can be the actions to decrease?
import imblearn
import mat73
...
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1answer
28 views
What reccent alternatives to LSTM are there for regression problems?
I have been working for a while on a regression problem - predicting the air pollution in a city based on meteorological features (humidity, temperature, wind velocity a.o.). I have trained an LSTM ...
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Test vector is move one step while test model Keras
I'm trying to build a LSTM model to predict metal concentrations (it's a multivariate problem) by Keras. The input shape is (24, 6), the output shape is (1,1), I use data from yesterday to predict the ...
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Scaling multi input LSTM
I have a single layer LSTM model with 300 time series which try to predict the next value for one time series, based on past 12 values of the 300 time series. 56 is the number of slices of length 12 ...
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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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23 views
LSTM model with exogenous factors
I have the following 3 columns in my dataset: 1.month, 2.day_of_week, 3.quantity.
I would like to predict the future values of quantity, having following variables as explanatory:
One-hot encoding of ...
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1answer
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Whats the minimum size sample required for a LSTM RNN model? [closed]
I have a data set of 100 rows x 100 to 300 columns. Will an LSTM RNN model work for my data or do I need more data?
If my sample size is a problem are there other RNN architectures capable of modeling ...
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Multiple linked features in an LSTM RNN
I have a self-taught knowledge of machine learning and have been using Keras in Python 3.8. I am trying to create a LSTM RNN model for prediction of time-dependant sequential data. My data is of ...
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How to correct format the dimension of the validation set - time series
I'm trying to understand how to add my validation data into my LSTM. At the moment I'm loading the train and the test set in the following way:
First of all I load my time series from a directory, ...
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Time-Series Cross-Validation for LSTM
Is it at all possible to separate my data into train/test sets with cross validation for time series data? I am experimenting with a LSTM model.
Also, I am hoping to prevent data leakage/peaking in ...
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Transformers (BERT) vs LSTM on Sentiment Analysis/NER - dataset sizes comparison
I am aware (continuously learning) of the advantages of Transformers over LSTMs.
At the same time, I was wondering from the viewpoint of size of the data needed, contrast of those two techniques, ...
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LSTM Autoencoder for variable length data
I have a dataset composed of data about client interactions. For each client I have a variable number of interactions ( client A has 2 interactions, client B has 1 interaction, etc...)
This ...
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LSTM for classification where each sample is a time series of fixed length
I am trying to classify the Pavia University HSI data using LSTM. The dataset is an image of dimension (610,340,103). There are 610, rows, 340 columns and each pixel has 103 values.
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How to improve the accuracy of the test set of LRCN-based video classification model
An existing LRCN-based video classification model consists of resnet152 provided by torchvision and an LSTM layer, and this model achieves 92% accuracy on the UCF-101 test set.
The input range of this ...
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1answer
29 views
ML algorithm for high dimensional time series forecasting
I'm trying to make a forecasting model for goods prices in an economy (trying to forecast inflation).
Dataset: has 300 goods prices % monthly variations for last 6 years. And also added $n$ ...
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1answer
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LSTM returns the same results for different inputs
Hey everyone,
I am working on a LSTM network in TensorFlow that predicts the values of the price-index of different product-categories in a month, based on those same values of the 12 months before. ...
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1answer
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Handle with very short and very long sequences with Neural Network
I am working on multi-class problem with sequences. My dataset is composed of sequences of data with different length.
E.g. 1500 labeled samples: 500 datapoint belongs to class A, 500 class B and 500 ...
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Predict next integer in sequence using ML.NET
Given a lengthy sequence of integers in the range of 0-1 I would like to be able to predict the next likely integer based on the previous sequence.
Example dataset: ...
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Should I use an LSTM model when the outcome is a different variable from the training data?
I am trying to model a health outcome as a function of climate variables. I have many observations of health outcomes at different times and locations (but NOT a sequence at one location). For each ...
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Recurrent models for asynchronous / mixed frequency time series
What are some of the RNN/LSTM models for handling mixed frequency/asynchronous time series data, such as macroeconomics, financial, precipitation, etc.?
So far I have found phased lstm from a similar ...
