N-BEATS
N-BEATS copied to clipboard
N-BEATS is a neural-network based model for univariate timeseries forecasting. N-BEATS is a ServiceNow Research project that was started at Element AI.
In the [MASE](https://github.com/ServiceNow/N-BEATS/blob/c746a4f13ffc957487e0c3279b182c3030836053/common/metrics.py#L28), I find that the shapes of `in_sample`, `out_sample`, and `forecasting` should be `time_o` or `time_in`, instead of `batch, time_i/o`. Moreover, does the MASE follow the formulation in...
For certain datasets, e.g. Yearly/Quarterly/Monthly M4 datasets, the quantity `history_size` is set to 1.5, leading `window_sampling_limit` to be 1.5 times of the horizon length. Yet, the input size could be...
Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!
Hi, In **N-BEATS**, inappropriate dependency versioning constraints can cause risks. Below are the dependencies and version constraints that the project is using ``` gin-config fire matplotlib numpy pandas patool torch...
class GenericBasis(t.nn.Module): """ Generic basis function. """ def __init__(self, backcast_size: int, forecast_size: int): super().__init__() self.backcast_size = backcast_size self.forecast_size = forecast_size def forward(self, theta: t.Tensor): return theta[:, :self.backcast_size], theta[:, -self.forecast_size:] is...
I am having trouble reproducing the results on the M4 dataset. I am getting the following error when running the notebook for the m4: --------------------------------------------------------------------------- ModuleNotFoundError Traceback (most recent call...
Are there any hyperparameters that are more important than others and do you have recommended ranges? I'm thinking of something similar to this, but for N-BEATS: 
Hi, I propose that the point forecast of N-BEATS be included in this repo for all the dataset evaluated in the original paper like it has been done for the...
Instead of parameters with grad=False, buffers is the way to go.
My data set has : time_idx, Date, Ticker, Open, high, low, close, Stock split and Dividends. My Time series data set is: training = TimeSeriesDataSet( combined_data[lambda x: x.time_idx