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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
exaly   +4 more sources

Woa-wtconv-kanformer for long term time series forecasting [PDF]

PLoS ONE
Multivariate time series analysis and prediction are of great significance in traffic management, weather forecasting and other practical applications.
Meng Ling Ming, Qi Wei Min, Dong Yi Fan, Zheng Yu Ning   +3 more
doaj   +3 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

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

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

Time Series Forecasting for Energy Consumption

Energies, 2022
Introduction In the last few years, there has been considerable progress in time series forecasting algorithms, which are becoming more and more accurate, and their applications are numerous and varied [...]
M. C. Pegalajar, L. G. B. Ruiz
doaj   +1 more source

A Framework for Imbalanced Time-Series Forecasting [PDF]

, 2022
Time-series forecasting plays an important role in many domains. Boosted by the advances in Deep Learning algorithms, it has for instance been used to predict wind power for eolic energy production, stock market fluctuations, or motor overheating. In some of these tasks, we are interested in predicting accurately some particular moments which often are
Luis P. Silvestrin, Leonardos Pantiskas, Mark Hoogendoorn   +2 more
openaire   +3 more sources

Forecasting of GPU Prices Using Transformer Method

Jurnal Sisfokom, 2023
GPU or VGA (graphic processing unit) is a vital component of computers and laptops, used for tasks such as rendering videos, creating game environments, and compiling large amounts of code.
Risyad Faisal Hadi, Siti Sa'adah, Didit Adytia   +2 more
doaj   +1 more source

Financial time series forecasting methods [PDF]

ITM Web of Conferences
The paper presents the development of time series forecasting algorithms based on the Integrated Autoregressive Moving Average Model (ARIMA) and the Fourier Expansion model.
Zinenko Anna, Stupina Alena
doaj   +1 more source