4 votes
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What is the "semantic level"?

In language theory, there are generally several admitted levels that can be studied in relation with one another or independently. The semantic level is the one dealing with the meaning of the text ("...
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  • 166
4 votes
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Which unsupervised learning technique can be used for anomaly detection in a time series?

So if I understood correctly: You have data from 2 sensors in time: Ar flow and BackGas Flow (SCCM, what is that?) You have that data for multiple products. 1 - Since it is relatively low dimensional, ...
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3 votes
Accepted

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

Usually, you need to ensure that your convolutions are causal, meaning that there is no information leakage from the future into the past. You could start by looking at this paper, which compares ...
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  • 1,078
3 votes
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Why is it harder to achieve good results using neural network based algorithms for multi step time series forecasting?

ANNs & RNNs can be used to create some great models in many different domains, including time-series forecasting. However, across all of these domains, they suffer from the problem of hyper-...
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2 votes

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

Given the (usual) higher architectural complexity of ML models compared to more classical forecasting models, ML models might also require more data, otherwise they might just overfit the training ...
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  • 33k
2 votes
Accepted

Inner working of Bidirectional RNNs

The second implementation looks more correct and inline with how Bidirectional is defined. Specifically, bidirectionality doen't change the forward/backward logic of either direction, and just merges (...
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  • 266
2 votes

How to predict time series with accuracy?

Things like this a really hot topic in research right now, and it's very difficult to get high accuracy on a chaotic system like the stock market. That being said, I would probably recommend ...
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2 votes

Do we need non-linear activation function in neural networks whose task isn't classification?

Yes, definitely. In the simplest example, predicting an output value for a time series is classification. You take in the previous time steps and classify what is the most likely next value. You could ...
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  • 1,324
2 votes

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

You are right CNN based models can outperform RNN. You can take a look at this paper where they compared different RNN models with TCN (temporal convolutional networks) on different sequence modeling ...
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  • 1,078
2 votes
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Feature extraction timeseries, model compatibility

there's a lot to un-pack in this question. Why do they only pick 500 rows? my guess: in order to keep the example running quickly. tsfresh usually takes a while to calculate its features. note that ...
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2 votes

How to deal with the time delay in reinforcement learning?

This problem has been formally termed as Delayed MDP (Katsikopoulos & Engelbrecht, 2003)[1] - the actions generated are not instantly applied to the environment and/or the captured observations ...
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  • 517
2 votes

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

You can use CNN for time-series data. The Convolutional Recurrent Neural Network (RCNN) is one of the examples. Convolutional layers basically extract features from images. It is not related to time-...
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2 votes

Is there a way to parallelise the RL training on multiple stocks to avoid the memory issue?

Take a look at: Deep Reinforcement Learning for Automated Stock Trading where the 30 Dow Jones stocks are trained using OpenAI Gym. The code is here and the published paper is here. An excerpt from ...
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2 votes
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Transformer model is very slow and doesn't predict well

Edit I just noticed that the model you are referring to is built very differently than the transformer from Attention is All You Need since it only uses one half of the architecture. Thus my answer ...
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  • 406
2 votes
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In a Temporal Convolutional Network, how is the receptive field different from the input size?

As you can see in Fig. 2 of the WaveNet paper the receptive field is 5, but the input size is larger (16). The receptive field defines what a single output neuron can see (see arrows in Fig. 2). The ...
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1 vote
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How to forecast multiple target attributes in Python?

In general, multi output models is not that different. I.e. As Raghu mentioned in commentary, you could train separate model for each output. There is even helper module in sklearn for that (...
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1 vote

How to deal with the time delay in reinforcement learning?

Most RL algorithms assume a discretization of time (although RL can also be applied to continuous-time problems [1]), i.e., in theory, it doesn't really matter what the actual time between consecutive ...
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  • 33k
1 vote
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Which type of feature extractor do you suggest to classify sensor data?

There could be multiple possible ways to extract the features. One would be to use RNNs for a temporal relationship as the input data is time-series.
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1 vote

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

I'm currently working with Temporal Convolution Networks (TCNs) for making predictions with time series data (link to article here: https://medium.com/@raushan2807/temporal-convolutional-networks-...
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1 vote

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

If I understand your problem correctly, you can test on just about any environment, and just omit parts of the observations to ensure your RNN is learning. For example, you can test on cartpole, ...
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  • 961
1 vote

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

Your idea is a good one. Another idea is to upsample or aggregate your data. For example, average by week if you generally have a couple of missing days in every week. A similar question on Stack ...
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1 vote

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

An interesting model I encountered in a course is Facebook Prophet. Prophet takes into account trends, seasonality, and holidays for its predictions. As you can probably guess, this is a model that ...
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1 vote
Accepted

What's the best architecture for time series prediction with a long dataset?

Yes, LSTM are ideal for this. For even stronger representational capacity, make your LSTM's multi-layered. Using 1-dimensional convolutions in a CNN is a common way to exctract information from time ...
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  • 261
1 vote

Predicting a day's data

Just for clarification: your description (1 sample per minute) does not match the example data (far fewer data points which is understandable, but also two data points in one minute which contradicts ...
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1 vote

When working with time-series data, is it wrong to use different time-steps for the features and target?

I don't know what kind of price data you're dealing with. I suppose the order of the data matters a lot, so my suggestion would be: Use LSTM as it handles time series better You can predict 3 ...
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1 vote
Accepted

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

Yes, you can fit any time series (with or without external variables) using HMM, but there are some constraints: It should follow the Markov property. There is some variance that other models are ...
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1 vote
Accepted

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

You need to have access to the 696th hour (or successive hours), otherwise, you cannot test your model. An alternative would be, for example, to train your model on the first 693 hours, validate it on ...
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  • 33k
1 vote

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

Definitely! but at that point it would be training a transformer-encoder (gpt2's architecture) and not GPT2 because GPT2 is defined by the weights / training procedure / data it was trained and not ...
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  • 2,229
1 vote

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

I have come across the same issue but in language. Where each input was a sentence, hence of different lengths. The easier solution is to just find the longest sequence, extract its length, and 0 ...
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