Next Article in Journal
Information-Centric Networking (ICN)
Previous Article in Journal
RFID RSS Fingerprinting System for Wearable Human Activity Recognition
Open AccessArticle

Unsteady Multi-Element Time Series Analysis and Prediction Based on Spatial-Temporal Attention and Error Forecast Fusion

School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
*
Author to whom correspondence should be addressed.
Future Internet 2020, 12(2), 34; https://doi.org/10.3390/fi12020034
Received: 24 December 2019 / Revised: 16 January 2020 / Accepted: 8 February 2020 / Published: 13 February 2020
Harmful algal blooms (HABs) often cause great harm to fishery production and the safety of human lives. Therefore, the detection and prediction of HABs has become an important issue. Machine learning has been increasingly used to predict HABs at home and abroad. However, few of them can capture the sudden change of Chl-a in advance and handle the long-term dependencies appropriately. In order to address these challenges, the Long Short-Term Memory (LSTM) based spatial-temporal attentions model for Chlorophyll-a (Chl-a) concentration prediction is proposed, a model which can capture the correlation between various factors and Chl-a adaptively and catch dynamic temporal information from previous time intervals for making predictions. The model can also capture the stage of Chl-a when values soar as red tide breaks out in advance. Due to the instability of the current Chl-a concentration prediction model, the model is also applied to make a prediction about the forecast reliability, to have a basic understanding of the range and fluctuation of model errors and provide a reference to describe the range of marine disasters. The data used in the experiment is retrieved from Fujian Marine Forecasts Station from 2009 to 2011 and is combined into 8-dimension data. Results show that the proposed approach performs better than other Chl-a prediction algorithms (such as Attention LSTM and Seq2seq and back propagation). The result of error prediction also reveals that the error forecast method possesses established advantages for red tides prevention and control.
Keywords: long short-term memory (LSTM); attention; harmful algal blooms (HABs); Chlorophyll-a (Chl-a); spatial; temporal; error forecast long short-term memory (LSTM); attention; harmful algal blooms (HABs); Chlorophyll-a (Chl-a); spatial; temporal; error forecast
MDPI and ACS Style

Wang, X.; Xu, L. Unsteady Multi-Element Time Series Analysis and Prediction Based on Spatial-Temporal Attention and Error Forecast Fusion. Future Internet 2020, 12, 34.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop