Interpretable forecasting with N-BEATS

This recipe introduces Neural Basis Expansion Analysis for Interpretable Time Series Forecasting (N-BEATS), a deep learning method for forecasting problems. We’ll show you how to train N-BEATS using PyTorch Forecasting and interpret its output.

Getting ready

N-BEATS is particularly designed for problems involving several univariate time series. So, we’ll use the dataset introduced in the previous chapter (check, for example, the Preparing multiple time series for a global model recipe):

import numpy as np
import pandas as pd
from gluonts.dataset.repository.datasets import get_dataset
from pytorch_forecasting import TimeSeriesDataSet
import lightning.pytorch as pl
from sklearn.model_selection import train_test_split
dataset = get_dataset('nn5_daily_without_missing', regenerate=False)
N_LAGS = 7
HORIZON = 7
datamodule = GlobalDataModule(data=dataset,
    n_lags=N_LAGS,
    ...