<p>There are a couple different ways to approach this problem. Based on the comments this sounds like a univariate/multi-step time series forecasting albeit across many different events.</p>
<p>First to clarify most deep learning for time series models/frameworks take data in the following format <code>(batch_size, n_historical_steps, n_feature_time_series)</code> and output the result in the format <code>(batch_size, n_forecasted_steps, n_targets)</code> .</p>
<p>Since this is a univariate forecasting problem <code>n_feature_time_series</code> would be one (unless I'm missing something). Now <code>n_historical_steps</code> is a hyper parameter we often optimize on as often the entire temporal history is not relevant to forecasting the next time n steps. You might want to try optimizing on that as well. However let say you choose to use the full temporal history then this would look like <code>(batch_size, 200, 1)</code>. Following this approach you might then have output shape of <code>(batch_size, 100, 1)</code>. You could then use a batch_size of 1000 to feed in all the different events at once (assuming of course you have a different validation/test set).This would give you an input shape of <code>(1000, 200, 1)</code> This is how you would likely do it for instance if you were going to use models like DA-RNN, LSTM, vanilla Transformer, etc.</p>
<p>There are some other models though that would create a learnable series embedding_id such as the <a href="https://arxiv.org/abs/1907.00235" rel="nofollow noreferrer">Convolutional Transformer Paper</a> or <a href="https://arxiv.org/abs/1704.04110" rel="nofollow noreferrer">Deep AR</a>. This is essentially a unique series identifier that would be associated with each event and the model would learn to forecast in the same pass on each.</p>
<p>I have models of both varieties implemented that you could use in <a href="https://github.com/AIStream-Peelout/flow-forecast" rel="nofollow noreferrer">Flow Forecast</a>. Though I don't have any detailed tutorials on this type of problem at the moment. I will also say also that in all honesty given that you only have 1000 BB events (each with only 300 univariate time steps) and the many variables in play at Basketball I doubt that you will be able to accomplish this task with any real degree of accuracy. I would guess you probably need at least 20k+ basketball event data to be able to forecast this type of problem well with deep learning at least.</p>
Predicting time series based on previous events using neural networks
<p>I want to see if the following problem can be solved by using neural networks: I have a database containing over 1000 basketball events, where the total score has been recorded every second from minute 5 till minute 20, and where the basketball games are all from the same league. This means that the events are occurring on different time periods. The data is afterwards interpolated to have the exact time difference between two timesteps, and thus obtaining exactly 300 points between minute 5 and minute 20. This can be seen here:
<a href="https://i.stack.imgur.com/f6B62.png" rel="nofollow noreferrer">Time series</a>. The final goal is to have a model that can predict the y values between t=15 till t=20 and use as input data the y values between t=5 and t=15. I want to train the model by using the database containing the 1000 events. For this I tried using the following network:</p>
<p><a href="https://i.stack.imgur.com/yt2wH.png" rel="nofollow noreferrer">input data vs output data</a></p>
<p><a href="https://i.stack.imgur.com/d7g0Q.png" rel="nofollow noreferrer">Neural network</a></p>
<p>The input data, that will be used to train the neural network model would have the shape (1000,200) and the output data, would have the shape (1000,100).
Can someone maybe guide me in the right direction for this and maybe give some feedback if this is a correct approach for such a problem, I have found some previous time series problems, but all of them were based on one large time series, while in this situation I have 1000 different time series.</p>