A Review of the Long Horizon Forecasting Problem in Time Series Analysis

arXiv:2506.12809 · cs.LG, cs.ET, cs.PF, stat.ML · Submitted 2025-06-15 · Read on arXiv

cs.LG, cs.ET, cs.PF, stat.ML

Submitted: 2025-06-15

Updated: 2026-09-06

Comments: Preprint only!

Code: https://github.com/hansk0812/Forecasting-Models

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Transformer — 26× in this paper · explained in 2 episodes
• Time Series Forecasting — 22× in this paper · explained in 3 episodes
• Ablation — 13× in this paper · explained in 1 episode
• Attention Mechanism — 7× in this paper · explained in 2 episodes
• Sparsity — 7× in this paper · explained in 4 episodes
• Aggregation — 6× in this paper · explained in 1 episode
• Accuracy — 4× in this paper · explained in 1 episode
• Embeddings — 4× in this paper · explained in 2 episodes
• Transformer Models — 4× in this paper · explained in 2 episodes
• Wavelet Transform — 4× in this paper · explained in 2 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Clustering — 2× in this paper · explained in 2 episodes
• Data Augmentation — 2× in this paper · explained in 7 episodes
• Direct Multi-Step — 2× in this paper
• Dynamic Network Model — 2× in this paper
• Foundation Model — 2× in this paper · explained in 3 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Mixture of Experts — 2× in this paper · explained in 2 episodes
• Transformer Architecture — 2× in this paper · explained in 2 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Auto-Regressive Integrated Moving Average — 1× in this paper
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Hybrid Models — 1× in this paper · explained in 2 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Iterative Multi-Step — 1× in this paper
• Latent Space — 1× in this paper · explained in 7 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Mean Average Error — 1× in this paper
• Neural Basis Expansion Analysis for Time Series — 1× in this paper
• Neural Hierarchical Interpolation for Time Series — 1× in this paper
• Robustness — 1× in this paper · explained in 5 episodes
• Seasonal Extraction in ARIMA Time Series — 1× in this paper
• State Space Models (SSMs) — 1× in this paper · explained in 4 episodes
• Theoretical Guarantees — 1× in this paper · explained in 2 episodes
• Vector Auto-Regression — 1× in this paper

Sources

• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting--Full Version
• Long-term Forecasting with TiDE: Time-series Dense Encoder
• Analyzing and Exploiting NARX Recurrent Neural Networks for Long-Term Dependencies
• Improving Long-Horizon Forecasts with Expectation-Biased LSTM Networks
• Reformer: The Efficient Transformer
• Deep Independently Recurrent Neural Network (IndRNN)
• Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
• iTransformer: Inverted Transformers Are Effective for Time Series Forecasting
• A Time Series is Worth 64 Words: Long-term Forecasting with Transformers
• N-BEATS: Neural basis expansion analysis for interpretable time series forecasting
• Coupled Oscillatory Recurrent Neural Network (coRNN): An accurate and (gradient) stable architecture for learning long time dependencies
• Neural Temporal Point Processes: A Review
• WaveNet: A Generative Model for Raw Audio
• A Multi-Horizon Quantile Recurrent Forecaster
• Effectively Modeling Time Series with Simple Discrete State Spaces
• FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting

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