  Large pre-trained models have been vital in recent advancements in domains
like language and vision, making model training for individual downstream tasks
more efficient and provide superior performance. However, tackling time-series
analysis tasks usually involves designing and training a separate model from
scratch leveraging training data and domain expertise specific to the task. We
tackle a significant challenge for pre-training a foundational time-series
model from multi-domain time-series datasets: extracting
  semantically useful tokenized inputs to the model
  across heterogenous time-series from different domains. We propose Large
Pre-trained Time-series Models (LPTM) that introduces a novel method of
\textit{adaptive segmentation} that automatically identifies optimal
dataset-specific
  segmentation strategy during pre-training. This enables
  LPTM to perform similar to or better than domain-specific state-of-art model
  when fine-tuned to different downstream time-series analysis tasks and under
zero-shot settings.
  LPTM achieves superior forecasting and time-series classification results
  taking up to 40% less data and 50% less training time
  compared to state-of-art baselines.
