Results 11 to 20 of about 17,777,604 (295)

Explicit Future Pattern-Enhanced Multivariate Time Series Forecasting [PDF]

Entropy
Multivariate time series forecasting requires models to infer future values and how the temporal structure evolves beyond the observation boundary. A central challenge is to define this evolution as an intermediate prediction and connect it to value ...
Yaokang Li, Yunan Wei, Jing Cai, Wenbin Cheng, Yiting Lin   +4 more
doaj   +2 more sources

Decomposition-Enhanced Network for financial time series forecasting [PDF]

Scientific Reports
The extreme non-stationarity, high noise levels, and multi-timescale coupling in financial futures markets pose major challenges for time series forecasting.
Jinyuan Huang, Qianqian Sun, Xinghua Zhang, Hong Du, Zheng Xu   +4 more
doaj   +2 more sources

Multi-Granular Embedding and Hybrid Encoder–Decoder Architecture with Temporal Linear Regression Bypass for Sensor-Based Time Series Forecasting in Smart Infrastructure [PDF]

Sensors
In this work, we propose a robust deep encoder–decoder neural network that integrates multiple deep sequence models for time series forecasting in sensor-based applications.
Hsu-Yung Cheng, Huan-Hsuan Lin, Chi-Lun Jiang, Chih-Chang Yu   +3 more
doaj   +2 more sources

Forecasting Techniques for Univariate Time Series Data: Analysis and Practical Applications by Category

Computer Sciences & Mathematics Forum
Effective forecasting is vital in various domains as it supports informed decision-making and risk mitigation. This paper aims to improve the selection of appropriate forecasting methods for univariate time series.
Leonard Dervishi, Antonios Raptakis, Gerald Bieber   +2 more
doaj   +2 more sources

Mamba time series forecasting with uncertainty quantification [PDF]

Machine Learning: Science and Technology
State space models, such as Mamba, have recently garnered attention in time series forecasting (TSF) due to their ability to capture sequence patterns. However, in electricity consumption benchmarks, Mamba forecasts exhibit a mean error of approximately ...
Pedro Pessoa, Paul Campitelli, Douglas P Shepherd, S Banu Ozkan, Steve Pressé   +4 more
doaj   +2 more sources

Improving forecasting by estimating time series structural components across multiple frequencies [PDF]

, 2014
Identifying the appropriate time series model to achieve good forecasting accuracy is a challenging task. We propose a novel algorithm that aims to mitigate the importance of model selection, while increasing accuracy.
Trapero Arenas, J.R, Trapero, Juan R., Trapero, Juan Ramon, Kourentzes, Nikos, Petropoulos, Fotios, Kourentzes, Nikolaos,, Trapero, Juan R.,, Kourentzes, Nikolaos, Petropoulos, Fotios,   +8 more
core   +5 more sources

Cheng Fuzzy Time Series Model to Forecast the Price of Crude Oil in Malaysia

Journal of Computing Research and Innovation, 2022
Crude oil is one of the important commodities to Malaysia. As a producer and exporter of oil and gas, Malaysia has gained high Gross Revenue from this sector. Crude oil is the global commodity and highly demanded.
Jasmani Bidin, Noorzila Sharif, Sharifah Fhahriyah Syed Abas, Ku Azlina Ku Akil, Nurul Aqilah Abdullah   +4 more
doaj   +3 more sources

Forecasting with time series imaging [PDF]

Expert Systems with Applications, 2020
Feature-based time series representations have attracted substantial attention in a wide range of time series analysis methods. Recently, the use of time series features for forecast model averaging has been an emerging research focus in the forecasting community.
Xixi Li, Yanfei Kang, Feng Li 0028
openaire   +4 more sources

Time Series Smoothing Improving Forecasting

Applied Computer Systems, 2021
Both statistical and neural network methods may fail in forecasting time series even operating on a great amount of data. It is an open question of which amount fits best to make sufficiently accurate forecasts on it. This implies that the length or time
Romanuke Vadim
doaj   +1 more source

Compression-Based Methods of Time Series Forecasting

Mathematics, 2021
Time series forecasting is an important research topic with many practical applications. As shown earlier, the problems of lossless data compression and prediction are very similar mathematically.
Konstantin Chirikhin, Boris Ryabko
doaj   +1 more source