make_future_timeseries

make_future_timeseries(idx, length_out, freq=None, force_regular=False)

Make future dates for a time series.

The function make_future_timeseries takes a pandas Series or DateTimeIndex and generates a future sequence of dates based on the frequency of the input series.

Parameters

Name	Type	Description	Default
idx	pd.Series or pd.DateTimeIndex	The `idx` parameter is the input time series data. It can be either a pandas Series or a pandas DateTimeIndex. It represents the existing dates in the time series.	required
length_out	int	The parameter `length_out` is an integer that represents the number of future dates to generate for the time series.	required
freq	str or pd.DateOffset	Frequency of the future dates. When `None`, the cadence is inferred from the input data (respecting `force_regular`). Accepts pandas offsets or human-friendly strings e.g. `"2 weeks"`.	`None`
force_regular	bool	The `force_regular` parameter is a boolean flag that determines whether the frequency of the future dates should be forced to be regular. If `force_regular` is set to `True`, the frequency of the future dates will be forced to be regular. If `force_regular` is set to `False`, the frequency of the future dates will be inferred from the input data (e.g. business calendars might be used). The default value is `False`.	`False`

Returns

Name	Type	Description
	pd.Series	A pandas Series object containing future dates.

Examples

import pandas as pd
import pytimetk as tk

# Works with a single date (must provide a length out and frequency if only
# 1 date is provided)
tk.make_future_timeseries("2011-01-01", 5, "D")

0   2011-01-02
1   2011-01-03
2   2011-01-04
3   2011-01-05
4   2011-01-06
dtype: datetime64[ns]

# DateTimeIndex: Generate 5 future dates (with inferred frequency)

dates = pd.Series(pd.to_datetime(['2022-01-01', '2022-01-02', '2022-01-03', '2022-01-04']))

future_dates_dt = tk.make_future_timeseries(dates, 5)
future_dates_dt

0   2022-01-05
1   2022-01-06
2   2022-01-07
3   2022-01-08
4   2022-01-09
dtype: datetime64[ns]

# Series: Generate 5 future dates
pd.Series(future_dates_dt).make_future_timeseries(5)

0   2022-01-10
1   2022-01-11
2   2022-01-12
3   2022-01-13
4   2022-01-14
dtype: datetime64[ns]

# Hourly Frequency: Generate 5 future dates
timestamps = ["2023-01-01 01:00", "2023-01-01 02:00", "2023-01-01 03:00", "2023-01-01 04:00", "2023-01-01 05:00"]

dates = pd.to_datetime(timestamps)

tk.make_future_timeseries(dates, 5)

0   2023-01-01 06:00:00
1   2023-01-01 07:00:00
2   2023-01-01 08:00:00
3   2023-01-01 09:00:00
4   2023-01-01 10:00:00
dtype: datetime64[ns]

# Monthly Frequency: Generate 4 future dates
dates = pd.to_datetime(["2021-01-01", "2021-02-01", "2021-03-01", "2021-04-01"])

tk.make_future_timeseries(dates, 4)

0   2021-05-01
1   2021-06-01
2   2021-07-01
3   2021-08-01
dtype: datetime64[ns]

# Quarterly Frequency: Generate 4 future dates
dates = pd.to_datetime(["2021-01-01", "2021-04-01", "2021-07-01", "2021-10-01"])

tk.make_future_timeseries(dates, 4)

0   2022-01-01
1   2022-04-01
2   2022-07-01
3   2022-10-01
dtype: datetime64[ns]

# Irregular Dates: Business Days
dates = pd.to_datetime(["2021-01-01", "2021-01-04", "2021-01-05", "2021-01-06"])

tk.get_frequency(dates)

tk.make_future_timeseries(dates, 4)

0   2021-01-07
1   2021-01-08
2   2021-01-11
3   2021-01-12
dtype: datetime64[ns]

# Irregular Dates: Business Days (Force Regular)
tk.make_future_timeseries(dates, 4, force_regular=True)

0   2021-01-07
1   2021-01-08
2   2021-01-09
3   2021-01-10
dtype: datetime64[ns]