All Questions
Tagged with neural-networks time-series
418 questions
2
votes
2
answers
704
views
Time series cross validation by reversing the series
I am trying to forecast revenue of a company, using neural networks. The response is a time series of monthly revenues from 11/2008 to 05/2016, and there are about 45 predictors (including lagged ...
- 23
0
votes
1
answer
785
views
When to use ANN with tensorflow?
I'm new to machine learning and tensorflow and I'm confused as to why (and when) to use the types of ANN (ie recurrent neural network) with tensorflow? I know RNN is good for sequences of data/time ...
- 3
2
votes
1
answer
942
views
Difference between particle filter (PF) and recurrent neural network (RNN) for time series
Both method are used to estimate time series from data. The question is, when should I use one method or other? Is any advantage to use one instead of the other?
I know that in a PF there is a hidden ...
- 129
9
votes
1
answer
3k
views
RNN learning sine waves of different frequencies
As a warm up with recurrent neural networks, I'm trying to predict a sine wave from another sine wave of another frequency.
My model is a simple RNN, its forward pass can be expressed as follow:
$$
\...
- 203
2
votes
0
answers
71
views
Perceptron trained on time series always predicting the same answer [duplicate]
Using the model from theano's tutorial, I'm training a 3-layers perceptron with log returns over a very large dataset (~55,000 points). The output's layer contains two neurons, one for each of the ...
5
votes
1
answer
1k
views
LSTM mimicking unseen time series data during testing
I have built a LSTM network which has been trained on a time series dataset (which is week-wise logged). The LSTM is able to make pretty accurate predictions as of now.
Training data seems to have ...
1
vote
1
answer
1k
views
Neural Network for Forecasting Time series
I have a dataset of monthly sales for the past 6 years. Significant attributes in the data set are:
Region, Nameplate, Segment MonthofSale and TotalSales.
I ...
- 121
4
votes
1
answer
2k
views
Choosing time points to run backpropagation through time
Suppose we're training a recurrent neural net (RNN) on a single, long time series using truncated backpropagation through time (BPTT). We make repeated sweeps through the time series, updating ...
- 33.2k
1
vote
1
answer
149
views
Time Series forecasting with useful predictor variables
I am playing with time series data related to a issue ticketing system. The system logs all open tickets at any one point and my task is to predict what the volume of open tickets will be in 5,10,15 ...
- 41
2
votes
0
answers
121
views
Variable-length time series - neural network [duplicate]
I have data about patient purchases - specifically, how late their fulfillment of prescriptions are, or if they filled them at all.
I want to feed this data into a neural network to classify them. ...
3
votes
3
answers
1k
views
Training a RNN on time series: How to cope with different sequence origins?
I am wondering if I should apply a recurrent neural network on my data. Data is EEG from sleep, and thus there is much information hidden in the temporal domain. Ergo, RNNs make sense.
Intro: I have ...
- 151
17
votes
2
answers
7k
views
What is the intuition behind a Long Short Term Memory (LSTM) recurrent neural network?
The idea behind Recurrent Neural Network (RNN) is clear to me. I understand it in the following way:We have a sequence of observations ($\vec o_1, \vec o_2, \dots, \vec o_n$) (or, in other words, ...
- 724
5
votes
0
answers
891
views
Recurrent neural network for real-valued prediction with exogenous variables [closed]
I have a problem that seems relatively straightforward yet I am stuck on how to proceed. I have several time series of variables $P(t), Q(t), E(t)$ and I want to train an RNN to predict $Q(t)$ given ...
1
vote
0
answers
964
views
Keras - Predictive ANN model converging on a single value. Overfitting? [duplicate]
I'm training an LSTM (using the Keras python library) to generate sequences. My X training data is a list of sequences, and the Y training data is a list of the final values of those sequences.
The ...
- 191
0
votes
0
answers
314
views
How can I use inputs of lower frequencies to predict an output of higher frequency?
I would like to use a NARX or a LSTM to use different type of data at different frequencies -for instance weekly, monthly and quarterly- to predict data sampled at a higher frequency -in my case, ...
- 93
2
votes
3
answers
4k
views
Delay issue in time series prediction
I am having an issue using neural networks to predict time series. Some predicted data fits with the expected data, as bellow: (In black the real time series and in blue the output of my neural ...
- 133
1
vote
0
answers
152
views
Forecasting a time series given three input series in R
My data frame consists of 3 input columns (factors 1, 2 and 3) and output column, i.e., revenue which are time varying parameters. I am trying to predict the revenue using neural networks for the ...
- 55
0
votes
1
answer
600
views
Suggestions for Neural Network Structure for Time-Series prediction with constant covariates
I've been working on a time series prediction problem and wondered if someone has run across a similar problem structure & can make a suggestion on how to structure the training data, network, or ...
- 1
2
votes
2
answers
1k
views
neural networks - Inputting a time series to a classification NN
I have a simple ANN that does the job of classification between two labels-:
Sick
Healthy
What I want to do is that input patient data ie. heart rate(ECG), EEG, etc which will be in the form of a ...
- 1,143
4
votes
0
answers
2k
views
Using AIC or cross-validated MSE for selecting neural network models for time series prediction
I trained two basic feed-forward neural networks on time series data. The first one uses the observation at time step $t$ to predict $t+1$. Hence, it only has one predictor variable. The second ...
- 3,112
10
votes
2
answers
7k
views
Best use of LSTM for within sequence event prediction
Assume the following 1 dimensional sequence:
A, B, C, Z, B, B, #, C, C, C, V, $, W, A, % ...
Letters A, B, C, .. here ...
- 201
3
votes
0
answers
1k
views
Example of Input & output vectors for time series Reccurent Neural Network training?
I've been searching for a while now to find the precise way to feed a Recurrent Neural Network (RNN, LSTM, GRU, ESN, Etc) with time series data with no real success.
Here is a question that was close,...
- 156
1
vote
1
answer
519
views
Theoritical Question: How to create Self learning demand forecasting algorithm
As suggested by Tim, gung, whuber I am editing this question and narrowing down the problem.
For hotels, I want to forecast number of room bookings that will happen x days before the day of check-in. ...
- 1,124
1
vote
1
answer
465
views
Neuralnet in R not giving me what I want to see
I am currently working on some research and we are trying to do some Time-Series prediction using neural networks. To get started, I was using the paper published by G. Peter Zhang (Time Series ...
- 11
1
vote
1
answer
3k
views
Simple Neural Network for time series prediction
I am creating a simple Multi-layered feed forward Neural Network using AForge.net NN library. My NN is a 3 Layered Activation Network trained with Supervised Learning
approach using BackPropogation ...
- 113
2
votes
0
answers
936
views
Time series data prediction with neural network model
I would like to predict stocks of a company for 6 months. I would like to use neural networks for this prediction.
Can anyone suggest how many hidden layers and hidden nodes to be used?
I have ...
- 616
3
votes
2
answers
932
views
Continue the predictions beyond the current data using time series neural network
I have a single time series variable and I want to train a neural network in a sort of auto-regressive fashion. specifically, I have data for water consumption that is changing with time
In the above ...
1
vote
1
answer
605
views
Train neural network for forecasting
I am trying to use time series neural network to predict future values. I have time series data from 2010-2014 and I need to predict the values from 2015-2020 using time series neural network. I am ...
1
vote
1
answer
925
views
Backtesting in neural network field
I'm new to the neural network field and I would like to understand how one can backtest a neural network trained with backpropagation methodology.
Particularly, I have a time series dataset and I ...
- 345
9
votes
2
answers
15k
views
Difference between Time delayed neural networks and Recurrent neural networks
I would like to use a Neural Network to predict financial time series. I come from an IT background and have some knowledge of Neural Networks and I have been reading about these:
TDNN
RNN
I have ...
- 93
4
votes
1
answer
2k
views
Resources for machine learning for time-dependent data
For the past year, I have spent the majority of my free time learning a variety of ML techniques (boosting, random forests, neural nets, SVMs etc.), but I have not been able to find a lot of material (...
- 927
8
votes
2
answers
6k
views
What is a good way to test a simple Recurrent Neural Network
I have coded up a simple real-value regression RNN in theano.
What kind of dataset should I test it on?
How should I go about testing it?
My structure is:
Univariate (for now) timeseries, $x_{in}(...
1
vote
2
answers
1k
views
Target and output in neural networks
In ANN the output squeezed using sigmoid function so the result is always between 1 and -1.
How am I supposed to calculate the error when the target value might be a big number?
For example I'm ...
- 111
2
votes
2
answers
2k
views
Neural network for time series forecasting- Single input Single output Theoretical proof needed
I am doing time series forecasting using neural networks. I have 2 approaches:
Forecasting in a auto regressive manner i.e based on time series lags as shown below:
...
- 21
4
votes
2
answers
409
views
Which method to use for load forecasting
I have smart meter data set that has consumption readings collected over a year and a half for every 30 mins. What I am trying to do is short term load forecasting. The data set has just three columns ...
- 53
16
votes
2
answers
12k
views
Timeseries analysis procedure and methods using R
I am working on a small project where we are trying to predict the prices of commodities (Oil, Aluminium, Tin, etc.) for the next 6 months. I have 12 such variables to predict and I have data from Apr,...
0
votes
1
answer
868
views
Neural Network - Classification from Time series
I'm a .Net programmer who is fairly new to neural networks, but I know some of the concepts.
I have connected .Net to my copy of Mathematica 10
This is a classification
Our business problem is ...
- 103
3
votes
2
answers
2k
views
Does the recurrent neural network require the length of input samples all the same
Theoretically, the training of RNN doesn't require that the samples must have the same time length, but it seems to me that some software or open-source requires that the input data has the same time ...
- 241
22
votes
2
answers
24k
views
Convolutional neural network for time series? [closed]
I would like to know if there exists a code to train a convolutional neural net to do time-series classification.
I have seen some recent papers (http://www.fer.unizg.hr/_download/repository/KDI-...
- 391
1
vote
2
answers
526
views
Problem on time-series
I am dealing with event data (recorded over a month) which gives out a binary response from a sensor when a door opens or closes - the time is noted at every instant and can also be represented in ...
- 11
2
votes
0
answers
480
views
How can I make sure that an LDA implementation works?
I am currently experimenting with neural nets for classification of on-line handwritten data (hence: not pixels, but time series data). To do so, I use several toolkits (internal development of my ...
- 1,767
0
votes
1
answer
145
views
Very different Neural Network test errors for same architecture
So I'm doing a time series prediction, and assessing the capability of the ANN to predict that time series. I am using Matlab's neural network toolbox functions, and the training parameters are the ...
- 1
7
votes
1
answer
10k
views
How to forecast multivariate time-series 'accurately' with a large number of unknown factors using R?
I am relatively new to statistics and not formally trained but have been given a complex problem to solve and need some guidance. I realise that I am out of my depth a bit here but would appreciate ...
- 111
7
votes
2
answers
3k
views
Implementing Neural Network for time series
I am currently working on neural networks for time series forecasting. My doubt is: do we need to take into account issues like trend, non-stationarity and seasonality while using neural networks ...
- 1,556
4
votes
1
answer
3k
views
Neural network for prediction
I am working on neural networks for a regression problem in R using packages like nnet, caret etc. I have split my data into ...
- 1,556
4
votes
2
answers
2k
views
Neural network for multivariate time series
I am currently analysing multivariate time series and have worked on VAR models in R. I need to know if there is any way to analyse it using neural networks in R.
PS: I am aware of the ...
- 1,556
2
votes
0
answers
332
views
are there any nonparametric forecasting methods?
Are there any good statistical non-parametric forecasting methods besides machine learning methods like neural networks/decision trees etc. for time series analysis ?
If so, are there any R packages ...
- 8,655
3
votes
1
answer
2k
views
Conditional restricted Boltzmann machines on a time series dataset
Preamble of the problem
I am currently trying to apply Conditional Restricted Boltzmann Machines on a time series dataset problem, in particular, the dataset constitutes of ...
- 379