3 votes
Accepted

Why is it harder to achieve good results using neural network based algorithms for multi step time series forecasting?

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-...
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3 votes

What approach should I take to model forecasting problem in machine learning?

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 ...
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2 votes

Why is it harder to achieve good results using neural network based algorithms for multi step time series forecasting?

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 ...
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  • 33.8k
2 votes

predict customer visit

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 ...
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2 votes

predict customer visit

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 ...
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  • 103
1 vote

For forecasting and trading control, given limited data, what AI approaches are well matched?

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 ...
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  • 1,390
1 vote

Why doesn't the LSTM model improve the time-series forecasting significantly with respect to the MLP model?

RNNs are known to be superior to MLP in case of sequential data, like yours. But complex models like LSTM and GRU require a lot of data to achieve their potential. I don't know about your data but you ...
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1 vote

What is a better approach to perform predictions of time-series several values ahead?

I have found nice tutorial in the Tensorflow documentation: https://www.tensorflow.org/tutorials/structured_data/time_series They implement and test both strategies....
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1 vote
Accepted

Should forecasting with neural networks only be treated as a supervised learning (regression) problem?

I think the choice of technique strongly depends on how fine-grained your forecast-predictions need to be. When it comes to forecasting by Reinforcement Learning (RL), one prominent example is the ...
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  • 705
1 vote

Using sigmoid in LSTM network for multi-step forecasting

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 ...
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  • 1,098
1 vote

Using sigmoid in LSTM network for multi-step forecasting

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.
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  • 381
1 vote

Using sigmoid in LSTM network for multi-step forecasting

Yes, due the input, output being constrained between zero and one that would be the only viable activation function.
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  • 11
1 vote

How do we choose the activation function for each hidden node?

TL;DR:One does not know ahead of time what hyper-parameters will achieve optimal performance. So what you need is an iterative implementation strategy: Implementation Strategy When working with ...
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  • 1,016
1 vote

How do we choose the activation function for each hidden node?

To know the form of your non-linear function, firstly you should define the type of problem you are dealing with such as an image classification task. Secondly, pick the activation functions based on ...
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