[HTML][HTML] Forecasting: theory and practice
Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …
uncertainty that surrounds the future is both exciting and challenging, with individuals and …
Deep learning for time series forecasting: Tutorial and literature survey
Deep learning based forecasting methods have become the methods of choice in many
applications of time series prediction or forecasting often outperforming other approaches …
applications of time series prediction or forecasting often outperforming other approaches …
The capacity and robustness trade-off: Revisiting the channel independent strategy for multivariate time series forecasting
Multivariate time series data comprises various channels of variables. The multivariate
forecasting models need to capture the relationship between the channels to accurately …
forecasting models need to capture the relationship between the channels to accurately …
Principles and algorithms for forecasting groups of time series: Locality and globality
P Montero-Manso, RJ Hyndman - International Journal of Forecasting, 2021 - Elsevier
Global methods that fit a single forecasting method to all time series in a set have recently
shown surprising accuracy, even when forecasting large groups of heterogeneous time …
shown surprising accuracy, even when forecasting large groups of heterogeneous time …
Large language models for time series: A survey
Large Language Models (LLMs) have seen significant use in domains such as natural
language processing and computer vision. Going beyond text, image and graphics, LLMs …
language processing and computer vision. Going beyond text, image and graphics, LLMs …
C2FAR: coarse-to-fine autoregressive networks for precise probabilistic forecasting
We present coarse-to-fine autoregressive networks (C2FAR), a method for modeling the
probability distribution of univariate, numeric random variables. C2FAR generates a …
probability distribution of univariate, numeric random variables. C2FAR generates a …
SutraNets: sub-series autoregressive networks for long-sequence, probabilistic forecasting
We propose SutraNets, a novel method for neural probabilistic forecasting of long-sequence
time series. SutraNets use an autoregressive generative model to factorize the likelihood of …
time series. SutraNets use an autoregressive generative model to factorize the likelihood of …
Development of fully convolutional neural networks based on discretization in time series classification
MH Tahan, M Ghasemzadeh… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Time Series Classification (TSC) is a crucial area in machine learning. Although applications
of Deep Neural Networks (DNNs) in this area have led to relatively good results, classifying …
of Deep Neural Networks (DNNs) in this area have led to relatively good results, classifying …
Classification of univariate time series via temporal abstraction and deep learning
N Itzhak, S Tal, H Cohen, O Daniel… - … Conference on Big …, 2022 - ieeexplore.ieee.org
Many time series classification algorithms have been proposed, including deep neural
networks based, which so far focused mainly on improving model architectures rather than …
networks based, which so far focused mainly on improving model architectures rather than …
Vq-ar: Vector quantized autoregressive probabilistic time series forecasting
Time series models aim for accurate predictions of the future given the past, where the
forecasts are used for important downstream tasks like business decision making. In …
forecasts are used for important downstream tasks like business decision making. In …