Step 1: Create a TimeGPT account and generate your API key
- Go to dashboard.nixtla.io to activate your free trial and set up an account.
- Sign in with Google, GitHub or your email
- Create your API key by going to โAPI Keysโ in the menu and clicking on โCreate New API Keyโ
- Your new key will appear. Copy the API key using the button on the right.

Step 2: Install Nixtla
In your favorite Python development environment: Installnixtla
with pip
:
Step 3: Import the Nixtla TimeGPT client
NixtlaClient
class providing your authentication API key.
validate_api_key
method.
Step 4: Start making forecasts!
Now you can start making forecasts! Letโs import an example using the classicAirPassengers
dataset. This dataset contains the monthly
number of airline passengers in Australia between 1949 and 1960. First,
load the dataset and plot it:
timestamp | value | |
---|---|---|
0 | 1949-01-01 | 112 |
1 | 1949-02-01 | 118 |
2 | 1949-03-01 | 132 |
3 | 1949-04-01 | 129 |
4 | 1949-05-01 | 121 |

๐ Data RequirementsFor further details go to Data Requirements.
- Make sure the target variable column does not have missing or non-numeric values.
- Do not include gaps/jumps in the datestamps (for the given frequency) between the first and late datestamps. The forecast function will not impute missing dates.
- The format of the datestamp column should be readable by Pandas (see this link for more details).
๐ Save figures made with TimeGPT Theplot
method automatically displays figures when in a notebook environment. To save figures locally, you can do:fig = nixtla_client.plot(df, time_col='timestamp', target_col='value')
fig.savefig('plot.png', bbox_inches='tight')
Forecast a longer horizon into the future
Next, forecast the next 12 months using the SDKforecast
method. Set
the following parameters:
df
: A pandas DataFrame containing the time series data.h
: Horizons is the number of steps ahead to forecast.freq
: The frequency of the time series in Pandas format. See pandasโ available frequencies. (If you donโt provide any frequency, the SDK will try to infer it)time_col
: The column that identifies the datestamp.target_col
: The variable to forecast.
timestamp | TimeGPT | |
---|---|---|
0 | 1961-01-01 | 437.837921 |
1 | 1961-02-01 | 426.062714 |
2 | 1961-03-01 | 463.116547 |
3 | 1961-04-01 | 478.244507 |
4 | 1961-05-01 | 505.646484 |

timegpt-1-long-horizon
model. Use this
model if you want to predict more than one seasonal period of your data.
For example, letโs forecast the next 36 months:
timestamp | TimeGPT | |
---|---|---|
0 | 1961-01-01 | 436.843414 |
1 | 1961-02-01 | 419.351532 |
2 | 1961-03-01 | 458.943146 |
3 | 1961-04-01 | 477.876068 |
4 | 1961-05-01 | 505.656921 |

Produce a shorter forecast
You can also produce a shorter forecast. For this, we recommend using the default model,timegpt-1
.
