2

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 do this with a RNN (Recurrent Neural Network) for example. If the activation functions are all linear, the nerual network is just a glorified linear regression....


2

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 are not immediately seen by the agent, as expected in an MDP. The delay can either be: a) A constant delay - Constant Delayed MDP (CDMDP) (Learning and planning ...


1

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 time steps is, but, in practice, you may have delays in the rewards or observations, so you cannot perform e.g. the TD updates immediately. One natural ...


1

A time series, usually, requires regular time intervals, but, from looking at your example, it seems that's not the case. You could try to use a MLP and give it as input the Time and Bitrate pairs and make it output the Class. The activation function is what makes your neural network produce its output, i.e. activate the neurons. The loss function calculates ...


1

How about a Temporal Convolutional Network? It feels like for such a long sequences having the recurrent/memory based approach is not too feasible. But, intuitively, the 1D convolutions should be able to pick out those rare features from your extremely long sequences. There are also claims that TCNs are comparable to RNNs in performance on common tasks, so ...


1

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 (MultiOutputRegressor) DecisionTreeRegressor from sklearn allows multiple outputs out-of-the box Any neural network framework allows any number of outputs In your ...


1

If you have sold only once or very few items you will need some prior input (domain knowledge). One term for search is intermittent time series. Here is a stored search. When you have many time series, related, and interest in both totals and single series, that is called hierarchical forecasting. One expert is here (the author of that blog was the founder ...


1

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.


1

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-bfea16e6d7d2). These types of networks, like other types of convolutional networks for time series, use a dilated convolution operation, which, unlike the standard ...


1

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, ignoring the velocity and angular velocity states. This way the MDP isn't actually Markovian and you'll need the RNN to learn.


1

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 Exchange: https://stats.stackexchange.com/questions/374935/how-to-deal-with-really-sparse-time-series-data-for-a-binary-classification-task


1

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 fits Facebook's needs very well. I'll give a brief introduction then provide a link where you can read more. Prophet fits a couple of functions of time ...


Only top voted, non community-wiki answers of a minimum length are eligible