The Journal of Time Series Analysis is the leading mathematical statistics journal focused on the important field of time series analysis.
This paper provides a selective guide to the literature on time series forecasting, covering the period 1982–2005 and summarizing over 940 papers.
We describe two automatic forecasting algorithms that have been implemented in the forecast package for R. The first is based on innovations state space models.
Feb 15, 2021 · In this article, we survey common encoder and decoder designs used in both one-step-ahead and multi-horizon time-series forecasting.
People also ask
Which is the best journal of forecasting?
What is the ranking of the journal of time series analysis?
What is the best time series forecasting method?
What is a forecasting journal?
Jan 9, 2024 · Time-series forecasting is a practical goal in many areas of science and engineering. Common approaches for forecasting future events often ...
The International Journal of Forecasting is the leading journal in its field. It is the official publication of the International Institute of Forecasters (IIF)
Jun 26, 2023 · Experimental results on nine real-life datasets show that LTSF-Linear surprisingly outperforms existing sophisticated Transformer-based LTSF models in all ...
Jul 30, 2019 · This package allows users to specify a KNN model and to generate its forecasts. The user can choose among different multi-step ahead strategies ...
Jul 1, 2021 · In this paper, we survey the most recent advances in supervised machine learning (ML) and high-dimensional models for time-series forecasting.
Abstract. This paper focuses on learning rate analysis of Nystr๖m regularization with sequential sub- sampling for τ-mixing time series.