Results 81 to 90 of about 223,849 (180)

Ten Things We Should Know About Time Series [PDF]

Time series data affect many aspects of our lives. This paper highlights ten things we should all know about time series, namely: a good working knowledge of econometrics and statistics, an awareness of measurement errors, testing for zero frequency ...
Les Oxley, Michael McAleer
core  

A Spatial–Temporal Time Series Decomposition for Improving Independent Channel Forecasting

Mathematics
Forecasting multivariate time series is a pivotal task in controlling multi-sensor systems. The joint forecasting of all channels may be too complex, whereas forecasting the channels independently may cause important spatial inter-dependencies to be ...
Yue Yu, Pavel Loskot, Wenbin Zhang, Qi Zhang, Yu Gao   +4 more
doaj   +1 more source

A Novel Encoder-Decoder Model for Multivariate Time Series Forecasting. [PDF]

Comput Intell Neurosci, 2022
Zhang H, Li S, Chen Y, Dai J, Yi Y.
europepmc   +1 more source

VARMA versus VAR for Macroeconomic Forecasting [PDF]

In this paper, we argue that there is no compelling reason for restricting the class of multivariate models considered for macroeconomic forecasting to VARs given the recent advances in VARMA modelling methodology and improvements in computing power.
Farshid Vahid, George Athanasopoulos
core  

Stacked LSTM for Short-Term Wind Power Forecasting Using Multivariate Time Series Data.

International Journal of Interactive Multimedia and Artificial Intelligence
Currently, wind power is the fast growing area in the domain of renewable energy generation. Accurate prediction of wind power output in wind farms is crucial for addressing the challenges associated the power grid. This precise forecasting enables grid
Manisha Galphade, V. B. Nikam, Biplab Banerjee, Arvind W. Kiwelekar, Priyanka Sharma   +4 more
doaj   +1 more source

Enhanced forecasting of shipboard electrical power demand using multivariate input and variational mode decomposition with mode selection

Scientific Reports
Accurate forecasting of shipboard electricity demand is essential for optimizing Energy Management Systems (EMSs), which are crucial for efficient and profitable operation of shipboard power grids. To address this challenge, this paper introduces a novel
Paolo Fazzini, Giuseppe La Tona, Matteo Diez, Maria Carmela Di Piazza   +3 more
doaj   +1 more source

Evaluation of interpretability methods for multivariate time series forecasting. [PDF]

Appl Intell (Dordr), 2022
Ozyegen O, Ilic I, Cevik M.
europepmc   +1 more source

Alternative methods for forecasting GDP [PDF]

An empirical forecast accuracy comparison of the non-parametric method, known as multivariate Nearest Neighbor method, with parametric VAR modelling is conducted on the euro area GDP. Using both methods for nowcasting and forecasting the GDP, through the
Dominique Guegan, Patrick Rakotomarolahy
core  

Evaluating Alternative Methods of Forecasting House Prices: A Post-Crisis Reassessment [PDF]

This paper compares the performance of different forecasting models of California house prices. Multivariate, theory-driven models are able to outperform a theoretical time series models across a battery of forecast comparison measures.
William D. Larson
core  

Client: Cross-variable linear integrated enhanced transformer for multivariate long-term time series forecasting

AI Open
Long-term time series forecasting (LTSF) is crucial in modern society, playing a pivotal role in facilitating long-term planning and developing early warning systems. While many Transformer-based models have recently been introduced for LTSF, a doubt has
Jiaxin Gao, Wenbo Hu, Dongxiao Zhang, Yuntian Chen   +3 more
doaj   +1 more source