TimeGPT is a production ready, generative pretrained transformer for time series. Itβs capable of accurately predicting various domains such as retail, electricity, finance, and IoT with just a few lines of code π.
nixtla
with pip
:
NixtlaClient
class providing your authentication API key.
validate_api_key
method.
AirPassengers
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 |
π 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).
π 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
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
pandasβ available
frequencies.
(If you donβt 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, letβs 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 |
timegpt-1
.