time-series-forecasting | Recurrent Neural Network Implementations for Time | Predictive Analytics library

I would distinguish the forecasting problem from the data sampling time problem. You are dealing substantially with missing data.

1. Forecasting problem: You may use any machine learning technique just ignoring missing data. If you are not familiar with machine learning, I would suggest you to use LASSO (least absolute shrinkage and selection operator), which has been demonstrated to have predicting power (see "Sparse Signals in the Cross-Section of Returns" by ALEX CHINCO, ADAM D. CLARK-JOSEPH, and MAO YE).

2. Missing imputation problem: In the first place you should consider the reason why you have missing data. Sometime it makes no sense to impute values because the information that the value is missing is itself important and should not be overridden. Otherwise you have multiple options, other than linear interpolation, to estimate the missing values. For example check the MATLAB function fillmissing.

Mean squared error can't be compared across datasets, because its magnitude depends on the units of the dataset. So you can't compare the MSE you're getting here to the MSE you see in example problems using other data.

One way to tell that the MSE value you're getting is reasonable is to look at the root mean squared error, which is in the scale of your original dataset. It's about 1000, and on average it looks like the forecasts are roughly 1000 away from the true values.

(this second part is a bit of a simplification, since RMSE penalizes large errors more than small errors, but it gives you an approximate check that the value you're getting is in the ballpark).

I just figured out how to change the date to a more standard format:

From the error message, it looks like you didn’t install the CUDA driver for your graphics card.

You can use caretForecast package. You can use any ML model which supported by caret including SVM.

to install the package: devtools::install_github("Akai01/caretForecast")

Here's the documentation.

Basically, you'll want to do something like train_test_split(values,test_size=.2,shuffle=False)

test_size=.2 tells the function to make the test size 20% of the input data (you can similarly specify trainset size with train_size=n, but in the absence of this specification the function will use 1-test_size, i.e. the complement of the test set).

shuffle=False tells the function not to randomly shuffle the order.

First, note that your GAMS assignment for u has a bug (sign error).

In GAMS you have to "unroll" the loop and construct a large system of simultaneous equations. Using data from your reference, this can look like:

It's pretty straightforward.

Create new dataframe (shold match your featue set)

filters = 64 means number of separate filters used is 64. Each filter will output 1 channel. i.e. here 64 filters operate on input to produce 64 different channels(or vectors). Hence filters parameter determines number of output channels.

kernel_size determines the size of the convolution window. Suppose kernel_size = 1 then each kernel will have dimension of in_channels x 1. Hence each kernel weight will be in_channels x 1 dimension tensor.

That means relu activation will be applied on the output of convolution operation.

Used to reduce depth channels with applying non-linearity. It will do something like weighted average across the channels while keeping receptive field.

In your eg: filters = 64, kernel_size = 1, activation = relu Suppose input feature map has size of 100 x 10(100 channels). Then the layer weight will of dimension 64 x 100 x 1. The output size will be 64 x 10.