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 |
---|---|
date | i64 |
1949-01-01 | 112 |
1949-02-01 | 118 |
1949-03-01 | 132 |
1949-04-01 | 129 |
1949-05-01 | 121 |
π Data RequirementsFor further details go to Data Requeriments.
- Make sure the target variable column does not have missing or non-numeric values.
- Do not include gaps/jumps in the timestamps (for the given frequency) between the first and late timestamps. The forecast function will not impute missing dates.
- The time column should be of type Date or Datetime.
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 polars offset alias, see the possible values
here.time_col
: The column that identifies the datestamp.target_col
: The variable to forecast.timestamp | TimeGPT |
---|---|
date | f64 |
1961-01-01 | 437.837921 |
1961-02-01 | 426.062714 |
1961-03-01 | 463.116547 |
1961-04-01 | 478.244507 |
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 |
---|---|
date | f64 |
1961-01-01 | 436.843414 |
1961-02-01 | 419.351532 |
1961-03-01 | 458.943146 |
1961-04-01 | 477.876068 |
1961-05-01 | 505.656921 |
timegpt-1
.