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]
159 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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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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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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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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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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4
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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 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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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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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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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 don't want to predict the output just by looking…
Erick Medina
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3
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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 predicting daily store sales and I am curious as to…
GiorgosMaragkopoulos
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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 specifically, it records the engine size. The…
luks
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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 array with two dimensions: one corresponding to…
Dawid
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