For questions related to time series analysis or forecasting in the context of AI and, in particular, ML.
See e.g. https://en.wikipedia.org/wiki/Time_series or https://machinelearningmastery.com/time-series-forecasting-supervised-learning/.
Questions tagged [time-series]
150 questions
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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 dataset looks like this:
ContextID Time_ms…
some_programmer
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6
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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 layers in a CNN model, as described below, which…
nilsinelabore
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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
HTK is a toolkit for building Hidden Markov Models…
vinjk
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5
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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 executed on the system, the state changes immediately…
jengmge
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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 deep, there is 99% probability that there will be…
Nulik
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5
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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 levels". Some examples:
HRHN can adaptively select…
MikelBa
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4
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When working with time-series data, is it wrong to use different time-steps for the features and target?
When working with time-series data, is it wrong to use daily prices as features and the price after 3 days as the target?
Or should I use the next-day price as a target, and, after training, predict 3 times, each time for one more day ahead (using…
George
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4
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What is the proper way to process continuous sequence data, such as time-series, using the Transformer?
What is the right way to input continuous, temporal (time-series) data into the Transformer? Assume we're using the basic TransformerBlock here.
Since data is continuous with no tokens, Token embedding can be directly skipped. How about positional…
meliksahturker
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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 describing time series forecasting where we have…
Dema Ushchapovskyy
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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 classical models like ARIMA) to achieve good results…
Majo_Jose
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3
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Positional Encoding of Time-Series features
I’m trying to use a Transformer Encoder I coded with weather feature vectors which are basically 11 features about the weather in the dimension [batch_size, n_features].
I have a data point per day, so this is a time-series but there are no…
Ouilliam
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3
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In a Temporal Convolutional Network, how is the receptive field different from the input size?
I'm playing around with TCN's lately and I don't understand one thing. How is the receptive field different from the input size?
I think that the receptive field is the time window that TCN considers during the prediction, so I guess the input size…
MASTER OF CODE
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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 this the best approach?
Petrus
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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 for analysing and predicting time series?
I am…
GKozinski
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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 that represent a certain change in conditions with…
user1563247
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