Questions tagged [time-series]

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

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1answer
189 views

Which unsupervised learning technique can be used for anomaly detection in a time series?

I've started working on anomaly detection in Python. My dataset is a time series one. The data is being collected by some sensors which record and collect data on semiconductor-making machines. My ...
5
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3answers
129 views

How to predict time series with accuracy?

I am trying to predict Forex time series. The nature of the market is that 80% of the time the price can not be predicted, but in 20% of the time it can be. For example, if the price drops down very ...
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1answer
1k views

What is the "semantic level"?

I am reading the paper Hierarchical Attention-Based Recurrent Highway Networks for Time Series Prediction (2018) by Yunzhe Tao et al. In this paper, they use several times the expression "semantic ...
4
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2answers
295 views

Can CNNs be applied to non-image data, given that the convolution and pooling operations are mainly applied to imagery?

When using CNNs for non-image (times series) data prediction, what are some constraints or things to look out for as compared to image data? To be more precise, I notice there are different types of ...
4
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1answer
170 views

How should I design the LSTM architecture for multivariate time series forecasting problems?

There is plenty of literature describing LSTMs in a lot of detail and how to use them for multi-variate or uni-variate forecasting problems. What I couldn't find though, is any papers or discussions ...
4
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2answers
354 views

Can hidden Markov models be used to model any time series data?

Can HMMs be used to model any time series data? Or does the data have to be that of a Markov process? In HTK documentation, I see that the first few lines state that it can model any time series ...
3
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2answers
117 views

Why is it harder to achieve good results using neural network based algorithms for multi step time series forecasting?

There are different kinds of machine learning algorithms, both univariate and multivariate, that are used for time series forecasting: for example ARIMA, VAR or AR. Why is it harder (compared to ...
3
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2answers
362 views

How to deal with the time delay in reinforcement learning?

I have a question regarding the time delay in reinforcement learning (RL). In the RL, one has state, reward and action. It is usually assumed that (as far as I understand it) when the action is ...
3
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1answer
136 views

Can non-sequential deep learning models outperform sequential models in time series forecasting?

Can a CNN (or other non-sequential deep learning models) outperform LSTM (or other sequential models) in time series data? I know this question is not very specific, but I experienced this when ...
3
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2answers
171 views

What kind of neural network architecture is suitable for variable length block-like time series data?

I'm not sure what this type of data is called, so I will give an example of the type of data I am working with: A city records its inflow and outflow of different types of vehicles every hour. More ...
3
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0answers
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 ...
2
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1answer
52 views

Feature extraction timeseries, model compatibility

I've got a timeseries with sensor data (e.g. accelerometer and gyroscope). I now want to extract the activity out of it (e.g. walking, standing, driving, ...). I Followed this Jupyter Notebook. But ...
2
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1answer
103 views

What are modern state-of-the-art solutions in prediction of time-series?

I wanted to ask you about the newest achievements in time series analysis (mostly prediction). What state-of-the-art solutions (as in frameworks, papers, related projects) do you know that can be used ...
2
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1answer
37 views

Predicting a day's data

I have a dataset containing timestamp and temperature. For each day, I have 1440 values viz., I have data for every minute of that day(60minutes * 24hrs = 1440). The Dataset looks like this: As an ...
2
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1answer
2k views

ValueError: Error when checking target: expected dense_3 to have shape (1,) but got array with shape (2,)

I am trying to build a CNN model on Keras. The data has a dimension of 921 rows × 10000 columns. Here is the code: ...
2
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0answers
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 ...
2
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0answers
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 ...
2
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0answers
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
75 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 ...
2
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0answers
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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0answers
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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0answers
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 ...
2
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0answers
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
28 views

Inner working of Bidirectional RNNs

I'm trying to understand how Bidirectional RNNs work. Specifically, I want to know whether a single cell is used with different states, or two different cells are used, each having independent ...
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1answer
27 views

Why use sin/cos to give periodicity in time series prediction

In this tutorial https://www.tensorflow.org/tutorials/structured_data/time_series#feature_engineering (scroll down a bit to "Time" heading), they take the sin/cos of the time index, and give ...
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1answer
45 views

Why doesn't the LSTM model improve the time-series forecasting significantly with respect to the MLP model?

I have recently started learning time series forecasting. I have a dataset of the weekly payment history of 10k clients over 1 year, and I want to predict the future 5 payments for a test set of 1k ...
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1answer
44 views

Rescaling time-series data with very spiky pattern for training data in LSTM network

I am working with some time-series hydrology data. Our goal is to forecast the time series forward, meaning predicting the data 1 month, 3 months ,6 months into the future. The data itself(image below)...
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1answer
47 views

What kind of neural network should I build to classify each instance of a time series sequence?

Let's say I have the time-series dataset below-left. I would like to train a model in such a way that, if I feed the model with an input like the test sequence below, it should be able to classify ...
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1answer
26 views

How to train an LSTM to classify based on rare historic event?

I want an LSTM to output one of two classes (Y, N), per frame, based on all the input so far. My original inputs are very long (~100000 samples long, far more than a standard LSTM training can handle ...
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1answer
51 views

Recommendations or resources for neural network/deep learning for time series application?

I know there are quite a few good deep learning books out there, but most explain neural networks and deep learning via application on images. If there are examples/code, they are often done on the ...
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1answer
265 views

How do I test an LSTM-based reinforcement learning model using any Atari games in OpenAI gym?

I am writing a couple of different reinforcement learning models based on Rainbow DQN or some PG models. All of them internally use an LSTM network because my project is using time series data. I ...
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1answer
36 views

How to exclude sections of bad data from time-series data before training an LSTM network

I am using LSTM network for predicting IOT time-series data receiving from un-reliable devices and networks. This results in several multiple sections [continuous streak of bad data for several days ...
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1answer
47 views

How can I test my trained network on the next unavailable hour?

I have data of 695 hours. I use the first 694 hours to train the network and I use 695th hour to validate it. Now my goal is to predict the next hour. How I can use my trained network to predict the ...
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1answer
494 views

Is it possible to use the GPT-2 model for time-series data prediction?

Is it possible and how trivial (or not) might it be (if possible) to retrain GPT-2 on time-series data instead of text?
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2answers
460 views

Why are RNNs better than MLPs at predicting time series data?

Understandably RNNs are very good at solving problems involving audio, video and text processing due to the arbitrary input's length of this sort of data. What I don't understand is why RNNs are also ...
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1answer
290 views

Train and Test Accuracy of GRU network not increasing after 2nd epoch

So I´m currently implementing my first neural network using GRUs as a model and Keras as an implementation since it´s pretty highlevel. My problem is about the classification of 8 hour long timeseries ...
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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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0answers
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 ...
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1answer
39 views

Recommended Time serie forecasting model for Fibonacci levels classification

I have a set of time series data which gives me fibonacci levels and the duration at which the value is at this level. Data structure to look like: ...
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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 ...
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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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1answer
57 views

Unix timestamps for Recurrent Neural Networks

I want to use RNN for classifying whole sequences of events, generated by website visitors. Each event has some categorical properties and a Unix timestamp: ...
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1answer
62 views

How to deal with very, very small time-series?

I have an ensemble of 231 time series, the largest among them being 14 lines long. The task at hand is to try to predict these time-series. But I'm finding this difficult due to the very small size of ...
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0answers
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 ...