  In order to support the advancement of machine learning methods for
predicting time-series data, we present a comprehensive dataset designed
explicitly for long-term time-series forecasting. We incorporate a collection
of datasets obtained from diverse, dynamic systems and real-life records. Each
dataset is standardized by dividing it into training and test trajectories with
predetermined lookback lengths. We include trajectories of length up to $2000$
to ensure a reliable evaluation of long-term forecasting capabilities. To
determine the most effective model in diverse scenarios, we conduct an
extensive benchmarking analysis using classical and state-of-the-art models,
namely LSTM, DeepAR, NLinear, N-Hits, PatchTST, and LatentODE. Our findings
reveal intriguing performance comparisons among these models, highlighting the
dataset-dependent nature of model effectiveness. Notably, we introduce a custom
latent NLinear model and enhance DeepAR with a curriculum learning phase. Both
consistently outperform their vanilla counterparts.
