Questions tagged [time-series]

For questions related to time series analysis or forecasting in the context of AI and, in particular, ML.

69 questions with no upvoted or accepted answers
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37 views

How to predict an event (or action) based on a window of time-series measurements?

I have an input vector $X$, which contains a series of measurements within a period, e.g. 100 measurements in 1 sec. The goal is to predict an event, let's say, moving forward, backward or static. I ...
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29 views

LSTM Recursive Forecast

I am confused about the way the LSTM networks work when forecasting with a horizon that is not finite, but I'm rather searching for a prediction in whatever time in future. In physical terms, I would ...
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34 views

Is there a technique for analyzing the relationship between time-series clusters?

I have two time-series datasets (temperature and speed of the vehicle). I will use Agglomerative Hierarchical Clustering and DTW to cluster both datasets. I am looking for a technique (like regression ...
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85 views

How to handle long sequences with transformers?

I have a time series sequence with 10 million steps. In step $t$, I have a 400 dimensional feature vector $X_t$ and a scalar value $y_t$ which I want to predict during inference time and I know during ...
2
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1answer
76 views

What method to identify markers in data series via machine learning

I have data that is collected from several different instruments simultaneously that is generally analyzed on a location-by-location basis. A skilled interpreter can identify "markers" in the data ...
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46 views

Should the RL agent be trained in an environment with real-world data or with a synthetic model?

I want to train a reinforcement learning agent in an environment with parameters (for example, the wind speed, sun irradiation, etc.) that change over time. I have recorded a limited amount of data ...
2
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0answers
27 views

Choosing neural network output for prediction (regression) of a dynamical system

I’m trying to train a neural network to approximate the output of a dynamical system $dy/dt=f\left(y(t), u(t) \right)$, namely, given $y(0)$ and $u(t_i), i=1,2...N$ I want the network to predict $y(...
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42 views

Are there any ways to model markov chains from time series data?

I want to make a thing that produces a stochastic process from time series data. The time series data is recorded every hour over the year, which means 24-hour of patterns exist for 365 days. What I ...
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131 views

Spike detection in time series using Artificial Neural Networks

I'm quite new in ANNs. I intend to use ANNs for predicting spike points in time series right before they happen. I've already used LSTM for another scenario, and I know that they can be used in ...
2
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1answer
243 views

How to train a LSTM model with multi dimensional data

I am trying to train my model using LTSM layer in Keras (python). I have some problems regarding the data representation and feeding it into the model. My data is 184 XY coodinates encoded as a numpy ...
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145 views

What are the possible neural network architecture for linear regression or time series regression?

I started modeling a linear regression problem using dense layers (layers.dense), which works fine. I am really excited, and now I am trying to model a time series linear regression problem using CNN, ...
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1answer
42 views

Continuous sequence data with Transformer model

What is the right way to input continuous, temporal(time series) data into Transformer network. Assume we're using the basic TransformerBlock here. Since data is continuous with no tokens, Token ...
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0answers
33 views

Forecasting of spatio-temporal event data

I’m currently working on my dissertation which is centred around forecasting social conflict events. I’m using data from GDELT (Global Database of Events, Tone, and Language) to develop my forecasting ...
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19 views

Is my dataset unlearnable, or is my LSTM model not smart enough?

I have time-series data obtained from a video. The data is composed of bitrate and corresponding label pairs for each timestamp: The distribution over the first 30 seconds is as follows: I have ...
1
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1answer
35 views

Predict time series from initial non-time dependant parameters

I'm trying to create an algorithm (neural network) that is able to predict a time series from a set of different parameters that are not given through time. Let's say I have a plane flying under the ...
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1answer
37 views

How to define a "don't care" class in time series classification in Pytorch?

This is a theoretical question. Setup I have a time series classification task in which I should output a classification of 3 classes for every time stamp t. All ...
1
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1answer
87 views

Model-based RL for time series data

I have time-series data. When I take an action, it impacts the next state, because my action directly determines the next state, but it is not known what the impact is. To be concrete: I have $X(t)$ ...
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0answers
22 views

What's the difference between RNNs and Feed Forward Neural Networks if a fixed size vector can preserve sequential information?

I was watching a Youtube video in which the problem of trying to predict the last word in a sentence was posed. The sentence was "I took my cat for a" and the last word was "walk"....
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44 views

How can I build a deep reinforcement learning model that can be trained with multiple time series datasets

I built a DRL model to trade stocks in the financial market but the number of observations is relatively small and I would like to increase it by training the same model with stocks from several ...
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0answers
30 views

How to calibrate model's prediction given past images?

I want to predict how open is the mouth given a face image. It's a regression problem (0= mouth not open, 1=mouth completely open). And something between 0 and 1 is also allowed. ConvNet works fine ...
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36 views

What are some good loss functions used to minimize extreme errors in regression and time series forecasting?

I'm working on a time series forecasting task, and, in some specific cases, I don't need perfect accuracy, but the network cannot by any means miss by a lot. So, in detriment of a smaller mean error, ...
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1answer
85 views

Why do we use a delay when feeding our input data to the echo state network?

I'm new to working with neural networks and have recently began implementing neural networks for time series forecasting in some of my work. I've been particularly using Echo State Networks and have ...
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0answers
36 views

Is there a difference between using 1d conv layers and 2d conv layers with kernel with size of 1 along other than time dimension?

Let's assume I use convolutional networks for time-series prediction. Data I feed to the network have 1 channel depth, height of number of periods and number of features is the width, so the frame ...
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0answers
47 views

Model for supervised sequence classification task

The Problem I am currently working on a sequence classification problem I try to solve with machine learning. The target variable is the current state of a system. This target variable is following a ...
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0answers
23 views

What's the best method to predict/generate signal from one sensor (source) to signal from another another (target)?

I was wondering what is the best method out there to find relationship between two 1D signals so that I can predict/generate one (source) from the other (target). For example, let's say that in ...
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1answer
66 views

time-series prediction : loss going down, then stagnates with very high variance

I am trying to design a model based on LSTM cells to do time-series prediction. The ouput value is an integer in [0,13]. I have noticed that one-hot encoding it and ...
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24 views

Generation of realistic real-valued sequences using Wasserstein GAN fails

My goal is to generate artificial sequences of real-valued data (e.g. time series) with GANs. Starting simple I tried to generate realistic sine-waves using a Wasserstein GAN. But even on this simple ...
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0answers
40 views

How to exploit translational symmetry for extrapolation in video generation using machine learning

I'll try to rephrase my problem in the context of video processing. Imagine that initial frame of video has some translational symmetry. The frame evolves according to an update rule. I generate a ...
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0answers
24 views

Deep Learning on time series tabular data

In this new book release, at the top of page 51 the authors mention that to do deep learning on time series tabular data the developer should structure the tensors such that the channels represent the ...
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0answers
36 views

Is it possible to use adversarial training to learn invariant features?

Given a set of time series data that are generated from different sites where all sites are investigating the same objective but with slightly different protocols. Is it possible to use adversarial ...
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1answer
88 views

Can HMM, MRF, or CRF be used to classify the state of a single observation, not the entire observation sequence?

I learn that the Viterbi algorithm used for Hidden Markov Model (HMM) can classify a sequence of hidden states from the corresponding observations; Markov Random Field (MRF) and Conditional Random ...
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2answers
18 views

Is seq2seq the best model when input/output sequences have fixed length?

I understand that seq2seq models are perfectly suitable when the input and/or the output have variable lengths. However, if we know exactly the input/output sequence lengths of the neural network. Is ...
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16 views

Dealing with images of multivariate time series

Assuming we have the following input multivariate series: number_of_samples, number_of_timestamps, number_of_features Upon conversion to images using any of the ...
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1answer
31 views

How to construct a model to predict the value of a time series $y_t$ that depends from other time series $\bar{X}_t$?

I would like to know what are the standard approach to construct a model to predict the value of a time series $y_t$ that depends from other time series $\bar{X}_t$. I use to see around that for this ...
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0answers
14 views

Time series forecasting with some challenges

I'm attempting to devise a strategy to make time series forecasts based on costs accumulated over time. My dataset contains about 7500 time-series sequences (call it an instance for now), each having ...
0
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0answers
30 views

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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0answers
12 views

Train separate AutoEncoder's on each class or one AE for all classes to learn features?

I'm working on a project where the dataset contains time series of three classes, depending on the shape of the series. I want to learn the representations of these series as vectors, so naturally I ...
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0answers
21 views

Positional Encoding in Transformer on multi-variate time series data hurts performance

I set up a transformer model that embeds positional encodings in the encoder. The data is multi-variate time series-based data. As I just experiment with the positional encoding portion of the code I ...
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0answers
29 views

How Long Short Term Memory (LSTM) work for time series classification?

I first got the concept of LSTM on how it works word to word prediction etc. However, I want to know how it work with the time-series classification. For example I have the follwing data (see image ...
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25 views

Time series forecasting for multiple objects with common features

I know the title of this question may raise an eyebrow, but I can't find the technical terms to define or investigate the actual problem. To demonstrate my problem with a simple hypothetical scenario: ...
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1answer
16 views
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13 views

End-to-end learning using LSTM-AE

I want to use prediction models like LSTM-AE to predict time-series data. The feature that the neural network should learn is in frequency between 40-60Hz. So, in order to learn the feature more ...
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1answer
20 views

What is the best strategies to perform forecasts via ML models?

Suppose one has time series (univariate or multivariate) and the goal is to predict values of these series several steps ahead. I see two possible strategies: Create a model (recurrent, convolutional,...
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0answers
12 views

Multiple Entities, Multivariate, Multi-step - Time Series Prediction - Python

My goal is to create a time series model with Multiple Entities - I have multiple products with pre orders and they all have the a similar bell shaped curve peeking at the release date of the product ...
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7 views

Feature selection by Simple regression vs finite impulse response (FIR) method (on TIME Series analysis)

We are working on prediction one company production estimation and the main field of works is like stock market prediction(Time series analysis and process data). So I have some comment on using ...
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11 views

Any good examples/projects of how to do time series forcasting for modern applications?

Time series have been widely analyzed by statistical models (e.g., ARIMA) and machine learning techniques (e.g., LSTM). While the textbooks have provided detailed explanations on the individual models/...
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11 views

What are the typical things in data that I would have to look for, when implementing survival models using machine learning?

Problem Scenario I am working on an industry-specific problem focussed on predicting the failure of a seal/gasket in the given time interval(T) in a high-pressure-compression environment. Whenever ...
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18 views

Good metrics and losses to use for Sequence-to-Sequence model for time-series prediction/forecasting

I am developing a sequence-to-sequence LSTM model for multi-step time series forecasting. I have the basic model working, so now I need to drill down on which loss function and evaluation metrics to ...
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0answers
9 views

How to measure(classify) the speed of oncoming traffic via Computer Vision and Neural Networks?

Suppose I have different videos of the same car sometimes moving slow, sometimes moving fast, say, at 50Kmph as slow and 60Kmph as fast. (Assume the background is a green screen and the car doesn't ...
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11 views

What does it mean when predicted results are constant values?

I'm practicing with some data with a LSTM neural nets to come up with predicted data, comparing with actual data. I generated an image to show what I came up with. The blue line is actual data, and ...