A Survey on Deep Learning for Time-Series Forecasting

Mahmoud, Amal; Mohammed, Ammar

doi:10.1007/978-3-030-59338-4_19

Cited by 47 publications

(25 citation statements)

References 71 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…LSTM was selected because it is one of the state-of-the-art ML models in time series forecasting. It has outperformed traditional neural networks in several applications 56 . Its ability to deal with short or long-term temporal dependencies can be promising in the SST time series modeling 57 .…”

Section: Experimental Protocolmentioning

confidence: 99%

Hybrid systems using residual modeling for sea surface temperature forecasting

Neto

Cavalcanti

Júnior

et al. 2022

Sci Rep

View full text Add to dashboard Cite

The sea surface temperature (SST) is an environmental indicator closely related to climate, weather, and atmospheric events worldwide. Its forecasting is essential for supporting the decision of governments and environmental organizations. Literature has shown that single machine learning (ML) models are generally more accurate than traditional statistical models for SST time series modeling. However, the parameters tuning of these ML models is a challenging task, mainly when complex phenomena, such as SST forecasting, are addressed. Issues related to misspecification, overfitting, or underfitting of the ML models can lead to underperforming forecasts. This work proposes using hybrid systems (HS) that combine (ML) models using residual forecasting as an alternative to enhance the performance of SST forecasting. In this context, two types of combinations are evaluated using two ML models: support vector regression (SVR) and long short-term memory (LSTM). The experimental evaluation was performed on three datasets from different regions of the Atlantic Ocean using three well-known measures: mean square error (MSE), mean absolute percentage error (MAPE), and mean absolute error (MAE). The best HS based on SVR improved the MSE value for each analyzed series by $$82.26\%$$ 82.26 % , $$98.93\%$$ 98.93 % , and $$65.03\%$$ 65.03 % compared to its respective single model. The HS employing the LSTM improved $$92.15\%$$ 92.15 % , $$98.69\%$$ 98.69 % , and $$32.41\%$$ 32.41 % concerning the single LSTM model. Compared to literature approaches, at least one version of HS attained higher accuracy than statistical and ML models in all study cases. In particular, the nonlinear combination of the ML models obtained the best performance among the proposed HS versions.

show abstract

Section: Experimental Protocolmentioning

confidence: 99%

Hybrid systems using residual modeling for sea surface temperature forecasting

Neto

Cavalcanti

Júnior

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Additionally, Mahmud and Mohammed performed a survey on the usage of deep learning algorithms for timeseries forecasting in 2021, which found that deep learning techniques like CNN and LSTM give superior prediction outcomes with lower error levels than other artificial neural network models [35]. Furthermore, their literature study discovered that merging many deep learning models greatly improved time-series prediction accuracy.…”

Section: Cnn and Lstm For Financial Time-series Predictionmentioning

confidence: 99%

Multivariate CNN‐LSTM Model for Multiple Parallel Financial Time‐Series Prediction

2021

View full text Add to dashboard Cite

At the macroeconomic level, the movement of the stock market index, which is determined by the moves of other stock market indices around the world or in that region, is one of the primary factors in assessing the global economic and financial situation, making it a critical topic to monitor over time. As a result, the potential to reliably forecast the future value of stock market indices by taking trade relationships into account is critical. The aim of the research is to create a time-series data forecasting model that incorporates the best features of many time-series data analysis models. The hybrid ensemble model built in this study is made up of two main components, each with its own set of functions derived from the CNN and LSTM models. For multiple parallel financial time-series estimation, the proposed model is called multivariate CNN-LSTM. The effectiveness of the evolved ensemble model during the COVID-19 pandemic was tested using regular stock market indices from four Asian stock markets: Shanghai, Japan, Singapore, and Indonesia. In contrast to CNN and LSTM, the experimental results show that multivariate CNN-LSTM has the highest statistical accuracy and reliability (smallest RMSE value). This finding supports the use of multivariate CNN-LSTM to forecast the value of different stock market indices and that it is a viable choice for research involving the development of models for the study of financial time-series prediction.

show abstract

“…Lu et al, for example, presented an ensemble structure of CNN-LSTM and shown that it is successful when used to forecast the Shanghai Composite Index [29]. In addition, Mahmud and Mohammed conducted a study in 2021 on the use of deep learning algorithms for timeseries forecasting, finding that deep learning methods such as CNN and LSTM provide better prediction results with lower error levels than other artificial neural network models [30]. Furthermore, their research found that combining several deep learning models significantly increased time-series prediction accuracy.…”

Section: -3-ensemble Of Cnn and Lstmmentioning

confidence: 99%

“…Furthermore, their research found that combining several deep learning models significantly increased time-series prediction accuracy. However, Mahmud and Mohammed also mentioned that CNN and LSTM's performance isn't always constant, with CNN outperforming LSTM at times and vice versa [30]. When dealing with time-series data made up of a collection of pictures, CNN seems to have a better predictive ability than LSTM, but when dealing with numerical data, LSTM looks to be superior.…”

Section: -3-ensemble Of Cnn and Lstmmentioning

confidence: 99%

GA-Optimized Multivariate CNN-LSTM Model for Predicting Multi-channel Mobility in the COVID-19 Pandemic

Widiputra

2021

Emerg Sci J

View full text Add to dashboard Cite

The primary factor that contributes to the transmission of COVID-19 infection is human mobility. Positive instances added on a daily basis have a substantial positive association with the pace of human mobility, and the reverse is true. Thus, having the ability to predict human mobility trend during a pandemic is critical for policymakers to help in decreasing the rate of transmission in the future. In this regard, one approach that is commonly used for time-series data prediction is to build an ensemble with the aim of getting the best performance. However, building an ensemble often causes the performance of the model to decrease, due to the increasing number of parameters that are not being optimized properly. Consequently, the purpose of this study is to develop and evaluate a deep learning ensemble model, which is optimized using a genetic algorithm (GA) that incorporates a convolutional neural network (CNN) and a long short-term memory (LSTM). A CNN is used to conduct feature extraction from mobility time-series data, while an LSTM is used to do mobility prediction. The parameters of both layers are adjusted using GA. As a result of the experiments conducted with data from the Google Community Mobility Reports in Indonesia that ranges from the beginning of February 2020 to the end of December 2020, the GA-Optimized Multivariate CNN-LSTM ensemble outperforms stand-alone CNN and LSTM models, as well as the non-optimized CNN-LSTM model, in terms of predicting human movement in the future. This may be useful in assisting policymakers in anticipating future human mobility trends. Doi: 10.28991/esj-2021-01300 Full Text: PDF

show abstract

A Survey on Deep Learning for Time-Series Forecasting

Cited by 47 publications

References 71 publications

Hybrid systems using residual modeling for sea surface temperature forecasting

Hybrid systems using residual modeling for sea surface temperature forecasting

Multivariate CNN‐LSTM Model for Multiple Parallel Financial Time‐Series Prediction

GA-Optimized Multivariate CNN-LSTM Model for Predicting Multi-channel Mobility in the COVID-19 Pandemic

Contact Info

Product

Resources

About