The field of time series forecasting and analysis is rapidly evolving, with a focus on developing innovative methods to handle non-stationary data, data drift, and complex temporal dependencies. Recent research has explored the use of novel architectures, such as the ChronoAdaptive Network (CANet) and the Gateformer, which integrate techniques like style-transfer and attention mechanisms to improve predictive accuracy. Additionally, there is a growing interest in developing interpretable models, such as PHeatPruner, which combines persistent homology and sheaf theory to provide actionable insights into complex data. Other notable trends include the use of variational autoencoders, longitudinal tabular transformers, and adaptive linear networks to preserve trend and seasonal information, detect data drift, and forecast multivariate time series. Noteworthy papers include CANet, which achieved a 42% reduction in MSE and a 22% reduction in MAE, and Gateformer, which achieved state-of-the-art performance across 13 real-world datasets. PHeatPruner is also notable for its ability to prune up to 45% of applied variables while maintaining or enhancing model accuracy.
Advances in Time Series Forecasting and Analysis
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CANet: ChronoAdaptive Network for Enhanced Long-Term Time Series Forecasting under Non-Stationarity
An Open-Source and Reproducible Implementation of LSTM and GRU Networks for Time Series Forecasting
A Machine Learning Approach For Bitcoin Forecasting
PHEATPRUNER: Interpretable Data-centric Feature Selection for Multivariate Time Series Classification through Persistent Homology
A Hybrid Framework for Real-Time Data Drift and Anomaly Identification Using Hierarchical Temporal Memory and Statistical Tests
Preserving Seasonal and Trend Information: A Variational Autoencoder-Latent Space Arithmetic Based Approach for Non-stationary Learning
TSRM: A Lightweight Temporal Feature Encoding Architecture for Time Series Forecasting and Imputation
Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics
Towards proactive self-adaptive AI for non-stationary environments with dataset shifts
Predicting Estimated Times of Restoration for Electrical Outages Using Longitudinal Tabular Transformers
Gateformer: Advancing Multivariate Time Series Forecasting through Temporal and Variate-Wise Attention with Gated Representations
Unlocking the Potential of Linear Networks for Irregular Multivariate Time Series Forecasting