An optimized model using LSTM network for demand forecasting
H Abbasimehr, M Shabani, M Yousefi - Computers & industrial engineering, 2020 - Elsevier
In a business environment with strict competition among firms, accurate demand forecasting
is not straightforward. In this paper, a forecasting method is proposed, which has a strong …
is not straightforward. In this paper, a forecasting method is proposed, which has a strong …
Improving time series forecasting using LSTM and attention models
H Abbasimehr, R Paki - Journal of Ambient Intelligence and Humanized …, 2022 - Springer
Accurate time series forecasting has been recognized as an essential task in many
application domains. Real-world time series data often consist of non-linear patterns with …
application domains. Real-world time series data often consist of non-linear patterns with …
A novel seasonal adaptive grey model with the data-restacking technique for monthly renewable energy consumption forecasting
S Ding, Z Tao, R Li, X Qin - Expert Systems with Applications, 2022 - Elsevier
To provide accurate renewable energy forecasts that adapt to the country's sustainable
development, a novel seasonal model combined with the data-restacking technique is …
development, a novel seasonal model combined with the data-restacking technique is …
A novel fractional structural adaptive grey Chebyshev polynomial Bernoulli model and its application in forecasting renewable energy production of China
Accurate mid-to-long term China's renewable energy forecasting is becoming more and
more important for integrating renewable energy systems with smart grid and energy …
more important for integrating renewable energy systems with smart grid and energy …
LSTM with particle Swam optimization for sales forecasting
Sales volume forecasting is of great significance to E-commerce companies. Accurate sales
forecasting enables managers to make reasonable resource allocation in advance. In this …
forecasting enables managers to make reasonable resource allocation in advance. In this …
Forecasting the industrial solar energy consumption using a novel seasonal GM (1, 1) model with dynamic seasonal adjustment factors
ZX Wang, ZW Wang, Q Li - Energy, 2020 - Elsevier
Due to influences of natural and social factors, the data of solar energy consumption
generally show the characteristic of seasonal fluctuations. In order to forecast data with …
generally show the characteristic of seasonal fluctuations. In order to forecast data with …
[HTML][HTML] Strategies for time series forecasting with generalized regression neural networks
This paper discusses how to forecast time series using generalized regression neural
networks. The main goal is to take advantage of their inherent properties to generate fast …
networks. The main goal is to take advantage of their inherent properties to generate fast …
A hybrid forecasting model using LSTM and Prophet for energy consumption with decomposition of time series data
S Arslan - PeerJ Computer Science, 2022 - peerj.com
For decades, time series forecasting had many applications in various industries such as
weather, financial, healthcare, business, retail, and energy consumption forecasting. An …
weather, financial, healthcare, business, retail, and energy consumption forecasting. An …
Multidimensional KNN algorithm based on EEMD and complexity measures in financial time series forecasting
G Lin, A Lin, J Cao - Expert Systems with Applications, 2021 - Elsevier
Stock time series forecasting is a universal purpose of academic researchers, even a slight
improvement in the accuracy of the forecast may have a fabulous impact on participants' …
improvement in the accuracy of the forecast may have a fabulous impact on participants' …
Hierarchical time series forecasting via support vector regression in the European travel retail industry
JP Karmy, S Maldonado - Expert Systems with Applications, 2019 - Elsevier
Times series often offers a natural disaggregation in a hierarchical structure. For example,
product sales can come from different cities, districts, or states; or be grouped by categories …
product sales can come from different cities, districts, or states; or be grouped by categories …