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[HTML][HTML] Deep learning for time series forecasting: Advances and open problems
A time series is a sequence of time-ordered data, and it is generally used to describe how a
phenomenon evolves over time. Time series forecasting, estimating future values of time …
phenomenon evolves over time. Time series forecasting, estimating future values of time …
A review on deep sequential models for forecasting time series data
Deep sequential (DS) models are extensively employed for forecasting time series data
since the dawn of the deep learning era, and they provide forecasts for the values required …
since the dawn of the deep learning era, and they provide forecasts for the values required …
Timemixer: Decomposable multiscale mixing for time series forecasting
Time series forecasting is widely used in extensive applications, such as traffic planning and
weather forecasting. However, real-world time series usually present intricate temporal …
weather forecasting. However, real-world time series usually present intricate temporal …
Deep learning-based effective fine-grained weather forecasting model
It is well-known that numerical weather prediction (NWP) models require considerable
computer power to solve complex mathematical equations to obtain a forecast based on …
computer power to solve complex mathematical equations to obtain a forecast based on …
LoRa based intelligent soil and weather condition monitoring with internet of things for precision agriculture in smart cities
Urbanization is expected to hold about 50% of the world population by 2050 and there will
be stress on available resources including food and freshwater. Further, inefficient utilization …
be stress on available resources including food and freshwater. Further, inefficient utilization …
Short-term wind power forecasting based on multivariate/multi-step LSTM with temporal feature attention mechanism
X Liu, J Zhou - Applied Soft Computing, 2024 - Elsevier
Precision enhancement for short-term wind power forecasting can alleviate negative impact
of the forecasting results on wind power generation. Due to complexities and nonlinearities …
of the forecasting results on wind power generation. Due to complexities and nonlinearities …
Towards dynamic spatial-temporal graph learning: A decoupled perspective
With the progress of urban transportation systems, a significant amount of high-quality traffic
data is continuously collected through streaming manners, which has propelled the …
data is continuously collected through streaming manners, which has propelled the …
IncepTCN: A new deep temporal convolutional network combined with dictionary learning for strong cultural noise elimination of controlled-source electromagnetic …
When the controlled-source electromagnetic (CSEM) data are contaminated by intense
cultural noise and the signal-to-noise ratio (S/N) is lower than 0 dB, the existing denoising …
cultural noise and the signal-to-noise ratio (S/N) is lower than 0 dB, the existing denoising …
Metaheuristic evolutionary deep learning model based on temporal convolutional network, improved aquila optimizer and random forest for rainfall-runoff simulation …
X Qiao, T Peng, N Sun, C Zhang, Q Liu, Y Zhang… - Expert Systems with …, 2023 - Elsevier
Accurate and reliable runoff prediction is of great significance to water resources
management, disaster monitoring and rational development and utilization of water …
management, disaster monitoring and rational development and utilization of water …
Deep learning for processing and analysis of remote sensing big data: A technical review
In recent years, the rapid development of Earth observation technology has produced an
increasing growth in remote sensing big data, posing serious challenges for effective and …
increasing growth in remote sensing big data, posing serious challenges for effective and …