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Time series forecasting using deep learning with 2 numFeatures

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israt fatema
israt fatema 2020 年 9 月 15 日
コメント済み: israt fatema 2020 年 10 月 29 日
How to use two input features to get 1 output using this example?
This example use 1 numFeatures, for example if i have time series data with 2 features, wind speed and temperature, how can i use this example to forecast weather?

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Anshika Chaurasia
Anshika Chaurasia 2020 年 9 月 30 日
Hi Israt,
It is my understanding that you have two features as input and want to train the network with this input.
You could change numFeatures = 2 while defining sequenceInputLayer function as shown:
numFeatures = 2; %take 2 features as input
numResponses = 1; %give 1 output
layers = [ ...
sequenceInputLayer(numFeatures)
lstmLayer(numHiddenUnits)
fullyConnectedLayer(numResponses)
regressionLayer];

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israt fatema
israt fatema 2020 年 10 月 29 日
Thank you for the response. In this case how can i read and load the time series data with these two features such as wind speed and temperature?
The example contains a single time series, the output is a cell array, where each element is a single time step and then reshaped the data to be a row vector.

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