When working with time series data, it is important to specify its frequency correctly, as this can significantly impact forecasting results. TimeGPT is designed to automatically infer the frequency of your timestamps. For commonly used frequencies, such as hourly, daily, or monthly, TimeGPT reliably infers the frequency automatically, so no additional input is required. However, for irregular frequencies, where observations are not recorded at consistent or regular intervals, such as the days the U.S. stock market is open, it is necessary to specify the frequency directly. TimeGPT requires that your data does not contain missing values, as this is not currently supported. In other words, the irregularity of the data should stem from the nature of the recorded phenomenon, not from missing observations. If your data contains missing values, please refer to our tutorial on missing dates. In this tutorial, we will show you how to handle irregular and custom frequencies in TimeGPT.

​
1. Import packages

First, we import the required packages and initialize the Nixtla client. 
import pandas as pd
import pandas_market_calendars as mcal
from nixtla import NixtlaClient

nixtla_client = NixtlaClient(
    # defaults to os.environ.get("NIXTLA_API_KEY")
    api_key = 'my_api_key_provided_by_nixtla'
)
👍 Use an Azure AI endpoint To use an Azure AI endpoint, remember to set also the base_url argument: nixtla_client = NixtlaClient(base_url="you azure ai endpoint", api_key="your api_key")

​
2. Handling regular frequencies

As discussed in the introduction, for time series data with regular frequencies, where observations are recorded at consistent intervals, TimeGPT can automatically infer the frequency of your timestamps if the input data is a pandas DataFrame. If you prefer not to rely on TimeGPT’s automatic inference, you can set the freq parameter to a valid pandas frequency string, such as MS for month-start frequency or min for minutely frequency. When working with Polars DataFrames, you must specify the frequency explicitly by using a valid polars offset, such as 1d for daily frequency or 1h for hourly frequency. Below is an example of how to specify the frequency for a Polars DataFrame. 
import polars as pl 

url = 'https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/air_passengers.csv'

polars_df = pl.read_csv(url, try_parse_dates=True)

fcst_df = nixtla_client.forecast(
    df=polars_df,
    h=12, 
    freq='1mo', 
    time_col='timestamp', 
    target_col='value', 
    level=[80, 95]
)

INFO:nixtla.nixtla_client:Validating inputs...
INFO:nixtla.nixtla_client:Preprocessing dataframes...
INFO:nixtla.nixtla_client:Restricting input...
INFO:nixtla.nixtla_client:Calling Forecast Endpoint...

nixtla_client.plot(polars_df, fcst_df, time_col='timestamp', target_col='value', level=[80,95])

​
3. Handling irregular frequencies

In this section, we will discuss cases where observations are not recorded at consistent intervals.

​
3.1 Load data

We will use the daily stock prices of Palantir Technologies (PLTR) from 2020 to 2023. The dataset includes data up to 2023-09-22, but for this tutorial, we will exclude any data before 2023-08-28. This allows us to show how a custom frequency can handle days when the stock market is closed, such as Labor Day in the U.S. 
url = 'https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/openbb/pltr.csv'
pltr_df = pd.read_csv(url, parse_dates=['date'])
pltr_df = pltr_df.query('date < "2023-08-28"')
pltr_df.head()
dateOpenHighLowCloseAdj CloseVolumeDividendsStock Splits
02020-09-3010.0011.419.119.509.503385844000.00.0
12020-10-019.6910.109.239.469.461242976000.00.0
22020-10-029.069.288.949.209.20550183000.00.0
32020-10-059.439.498.929.039.03363169000.00.0
42020-10-069.0410.188.909.909.90908640000.00.0
We will forecast the adjusted closing price, which represents the stock’s closing price adjusted for corporate actions such as stock splits, dividends, and rights offerings. Hence, we will exclude the other columns from the dataset. 
pltr_df = pltr_df[['date', 'Adj Close']]

nixtla_client.plot(pltr_df, time_col = "date", target_col = "Adj Close")

​
3.2 Define the frequency

To define a custom frequency, we will first extract and sort the dates from the input data, ensuring they are in the correct datetime format. Next, we will use the pandas_market_calendars package, specifically the get_calendar method, to obtain the New York Stock Exchange (NYSE) calendar. Using this calendar, we can create a custom frequency that includes only the days the stock market is open. 
dates = pd.DatetimeIndex(sorted(pltr_df['date'].unique())) # sort all dates in the dataset

nyse = mcal.get_calendar('NYSE') # New Yor Stock Exchange calendar
Note that the days the stock market is open need to include all the dates in the input data plus the forecast horizon. In this example, we will forecast 7 days ahead, so we need to make sure our trading days include the last date in the input data as well as the next 7 valid trading days. To avoid dealing with holidays or weekends during the forecast horizon, we will specify an end date well beyond the forecast horizon. For this example, we will use January 1, 2024, as a safe cutoff. 
trading_days = nyse.valid_days(start_date=dates.min(), end_date="2024-01-01").tz_localize(None)
Now, with the list of trading days, we can identify the days the stock market is closed. These are all weekdays (Monday to Friday) within the range that are not trading days. Using this information, we can define a custom frequency that skips the stock market’s closed days. 
all_weekdays = pd.date_range(start=dates.min(), end="2024-01-01", freq='B')

closed_days = all_weekdays.difference(trading_days)

custom_bday = pd.offsets.CustomBusinessDay(holidays=closed_days)

​
3.3 Forecast with TimeGPT

With the custom frequency defined, we can now use the forecast method, specifying the custom_bday frequency in the freq argument. This will make the forecast respect the trading schedule of the stock market. 
fcst_pltr_df = nixtla_client.forecast(
    df=pltr_df, 
    h=7, 
    freq=custom_bday,
    time_col='date', 
    target_col='Adj Close',
    level=[80, 95]
)

INFO:nixtla.nixtla_client:Validating inputs...
INFO:nixtla.nixtla_client:Preprocessing dataframes...
INFO:nixtla.nixtla_client:Querying model metadata...
INFO:nixtla.nixtla_client:Restricting input...
INFO:nixtla.nixtla_client:Calling Forecast Endpoint...
📘 Available models in Azure AI If you are using an Azure AI endpoint, please be sure to set model="azureai": nixtla_client.forecast(..., model="azureai") For the public API, we support two models: timegpt-1 and timegpt-1-long-horizon. By default, timegpt-1 is used. Please see this tutorial on how and when to use timegpt-1-long-horizon. 
nixtla_client.plot(pltr_df, fcst_pltr_df, time_col = "date", target_col = "Adj Close", level=[80, 95], max_insample_length = 180)

fcst_pltr_df[['date']].head(7)
date
02023-08-28
12023-08-29
22023-08-30
32023-08-31
42023-09-01
52023-09-05
62023-09-06
Note that the forecast excludes 2023-09-04, which was a Monday when the stock market was closed for Labor Day in the United States.

​
4. Summary

Below are the key takeaways of this tutorial:
  • TimeGPT can reliably infer regular frequencies, but you can override this by setting the freq parameter to the corresponding pandas alias.
  • When working with polars data frames, you must always specify the frequency using the correct polars offset.
  • TimeGPT supports irregular frequencies and allows you to define a custom frequency, generating forecasts exclusively for the specified dates.