3

This is an interesting problem, the answer of which is highly coveted for obvious reasons. The production of an answer in this public space is appropriate, provided one believes in a more level distribution of assets across those alive and is comfortable with rewarding technical people for the abilities they developed while others might have been engaging ...


3

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-parameter optimization. Because neural networks are so flexible, it is not clear, at the outset, which arrangement of neurons will be most effective to solve a given ...


2

In general, this type of problem is called a regression problem since the target variable (i.e. travel time) can take any value in a continuous domain. In theory, you can use any regression algorithms (a subset supervised learning techniques) to solve this problem. Some of the most popular ones are linear regression, K-nearest neighbor (regressor), and ...


2

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 dataset. Furthermore, online learning (or training) of a neural network using stochastic gradient descent (that is, one example at a time) might also be ...


1

You should not limit yourself to sigmoid as activation function on the last layer. Usually you're normalizing your dataset, but when you're testing/evaluating the model you're applying the inverse of the scaling transformation to the predictions, so you could easily use tanh which is defined on [-1, 1]


1

You have a problem in your code, you want to use "sigmoid" in the last layer. Fot the code you are showin you are using linear activation in the last layer.


1

Yes, due the input, output being constrained between zero and one that would be the only viable activation function.


1

An 'AI'* is only as smart as the information you give it You've got to add your own knowledge of the situation into this. Currently we have a transaction id which only really tells us that there is a transaction, a card number (identifying a user, I assume) and a date. The date can probably tell you most - what day of the week was it? What season (most sales ...


1

What interests us in this problem are only the intervals for 1 person. Lets say that we want to train a neural network on recognizing the simple pattern in date differences. This would mean that we could train the neural network on series of purchase histories of multiple people. That means that one possible input is the previous intervals: in your case the ...


1

Unclear from your description how RL is useful. RL is a technique that allows your model learn interactive by trial and error. Where is your "trial and error" in your problem? Stock price prediction sounds more like a regression problem to me. It can be done by DL or many other methods. Probably a shallow neural network would work well for you.


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