time-series-forecasting | becoming increasingly common for organizations to collect | Time Series Database library

You have to provide an input_shape to the first layer. In your case this is tf.keras.layers.Bidirectional() and not tf.keras.layers.LSTM.

Two alternatives are:

• First call your model with actual data
• Call model.build(input_shape) before calling model.compile()

Since you want the previous 12 reads, the first 11 will be NaN. You need more rows than 12 before you get a moving average of 12. You can see this on the link you provided. The chart of MA doesn't start up right away.

this is because your network is build to output 3D sequences of shape (None, 3, 1) while your targets have shape (None, 2, 1)

The best and automated way to handle this situation correctly is to build an encoder-decoder structure... Below the example:

Pandas dates default to datetime64[ns]. So you don't want to compare them to datetime.date objects. Instead, you can just use a date string and pandas will handle the comparison corectly. Also, if you use loc to specify the rows and columns, you will get a cleaner syntax than in your examples.

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")