utils.py

from pandas import DataFrame
from pandas import concat

# This converts a time series into an inputs and outpus for training
# Learn more at:
# https://machinelearningmastery.com/convert-time-series-supervised-learning-problem-python/
def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):
	"""
	Frame a time series as a supervised learning dataset.
	Arguments:
		data: Sequence of observations as a list or NumPy array.
		n_in: Number of lag observations as input (X).
		n_out: Number of observations as output (y).
		dropnan: Boolean whether or not to drop rows with NaN values.
	Returns:
		Pandas DataFrame of series framed for supervised learning.
	"""
	n_vars = 1 if type(data) is list else data.shape[1]
	df = DataFrame(data)
	cols, names = list(), list()
	# input sequence (t-n, ... t-1)
	for i in range(n_in, 0, -1):
		cols.append(df.shift(i))
		names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]
	# forecast sequence (t, t+1, ... t+n)
	for i in range(0, n_out):
		cols.append(df.shift(-i))
		if i == 0:
			names += [('var%d(t)' % (j+1)) for j in range(n_vars)]
		else:
			names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]
	# put it all together
	agg = concat(cols, axis=1)
	agg.columns = names
	# drop rows with NaN values
	if dropnan:
		agg.dropna(inplace=True)
	return agg