Foundation models for time series analysis: A tutorial and survey
Time series analysis stands as a focal point within the data mining community, serving as a
cornerstone for extracting valuable insights crucial to a myriad of real-world applications …
cornerstone for extracting valuable insights crucial to a myriad of real-world applications …
Deep time series models: A comprehensive survey and benchmark
Time series, characterized by a sequence of data points arranged in a discrete-time order,
are ubiquitous in real-world applications. Different from other modalities, time series present …
are ubiquitous in real-world applications. Different from other modalities, time series present …
Chronos: Learning the language of time series
We introduce Chronos, a simple yet effective framework for pretrained probabilistic time
series models. Chronos tokenizes time series values using scaling and quantization into a …
series models. Chronos tokenizes time series values using scaling and quantization into a …
A survey on time-series pre-trained models
Time-Series Mining (TSM) is an important research area since it shows great potential in
practical applications. Deep learning models that rely on massive labeled data have been …
practical applications. Deep learning models that rely on massive labeled data have been …
[HTML][HTML] TimeGPT in load forecasting: A large time series model perspective
Abstract Machine learning models have made significant progress in load forecasting, but
their forecast accuracy is limited in cases where historical load data is scarce. Inspired by …
their forecast accuracy is limited in cases where historical load data is scarce. Inspired by …
AutoCTS++: zero-shot joint neural architecture and hyperparameter search for correlated time series forecasting
Sensors in cyber-physical systems often capture interconnected processes and thus emit
correlated time series (CTS), the forecasting of which enables important applications …
correlated time series (CTS), the forecasting of which enables important applications …
Time-moe: Billion-scale time series foundation models with mixture of experts
Deep learning for time series forecasting has seen significant advancements over the past
decades. However, despite the success of large-scale pre-training in language and vision …
decades. However, despite the success of large-scale pre-training in language and vision …
Self-supervised learning for accelerometer-based human activity recognition: A survey
Self-supervised learning (SSL) has emerged as a promising alternative to purely supervised
learning, since it can learn from labeled and unlabeled data using a pre-train-then-fine-tune …
learning, since it can learn from labeled and unlabeled data using a pre-train-then-fine-tune …
Visionts: Visual masked autoencoders are free-lunch zero-shot time series forecasters
Foundation models have emerged as a promising approach in time series forecasting (TSF).
Existing approaches either repurpose large language models (LLMs) or build large-scale …
Existing approaches either repurpose large language models (LLMs) or build large-scale …
Timedit: General-purpose diffusion transformers for time series foundation model
With recent advances in building foundation models for texts and video data, there is a surge
of interest in foundation models for time series. A family of models have been developed …
of interest in foundation models for time series. A family of models have been developed …