These are some key concepts related to time series forecasting, designed to help you better understand and leverage the capabilities of TimeGPT.
TimeGPT
is the first foundation model for time
series forecasting. TimeGPT
was trained on billions of observations
from publicly available datasets across multiple domains and can produce
accurate forecasts for new time series without additional training,
using only historical values as inputs. The model ‘reads’ time series
data similarly to how humans read a sentence—sequentially from left to
right. It looks at windows of past data, which we can think of as
‘tokens’, and predicts what comes next. This prediction is based on
patterns the model identifies in past data and extrapolates into the
future.
TimeGPT
processes time series data in chunks. Each data point in a
series can be thought of as a ‘token’, akin to how individual words or
characters are treated in natural language processing (NLP).
TimeGPT
undergoes additional training to adapt it for a
specific dataset. Initially, TimeGPT
can operate in a zero-shot
manner, meaning it can generate forecasts as-is. While this zero-shot
approach provides a solid baseline, the performance of TimeGPT
can
often be improved through fine-tuning. During this process, the
TimeGPT
model undergoes additional training using the specific
dataset, starting from the pre-trained parameters. The updated model
then produces the forecasts.
Learn how to fine-tune
TimeGPT