A Wavelet-Based Method for Time Series Forecasting

Dominguez, Gabriela; Guevara, Miguel; Mendoza, Marcelo; Zamora, Juan

doi:10.1109/sccc.2012.19

Cited by 5 publications

(6 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The data forecasting of time series technique using the RNN algorithm, could also be done with the sliding windows method [38], [39] . This method allowed more accurate periodic data predictions, and the prediction process could be determined for several periods x i [27], [40], [41]. he RNN architecture design with the sliding windows method was shown in Figure 3.…”

Section: Sliding Windows Techniquementioning

confidence: 99%

“…RNN model can recognize fluctuating patterns for a fairly high level of data fluctuation. There were several studies related to the optimization of RNN model in prediction case, including the comparison of meurowavelet model to predict the short-term of stock returns from high-prequency of financial data [26], [27], the use of RNN method in network performance is to measure the river flows [20]. The combination of ERNN method with Cooperative coevolution method produces a more stable predictive value comparing to the neuroevolution method [28].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Time Series Data Prediction using Elman Recurrent Neural Network on Tourist Visits in Tanah Lot Tourism Object

Sugiartawan,

Hartati*

2019

IJEAT

View full text Add to dashboard Cite

The prediction of time series data is a forecast using the analysis of a relationship pattern between what will be predicted (prediction) and the time variable. The prediction process using the recurrent neural network (RNN) model could recognize and learn the data pattern of time series, but the presence of fluctuations in data makes the introduction of data patterns difficult to be learned. The data used for forecasting are tourist visits to Tanah Lot Bali tourist attraction for 10 years (2008-2017). The training process uses the RNN method on high fluctuating data, which requires a relatively long time in recognizing and studying the data patterns. Modification of the RNN method on learning rate and momentum by using dynamic values, can shorten learning time. The results showed the learning time using the RNN dynamic value, smaller than the variants of the RNN method such as the RNN Elman, Jordan RNN, Fully RNN, LSTM and the feedforward method (Backpropagation). The resulting error value is 0,05105 MSE. This value is smaller than the Fully RNN, Jordan RNN, LSTM and Feedforward methods. The elman method has the shortest training time among other models. The purpose of this research is to make a prediction design consisting of sliding windows techniques, training with neural network models and validation of results with k-fold cross-validation.

show abstract

Section: Sliding Windows Techniquementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Time Series Data Prediction using Elman Recurrent Neural Network on Tourist Visits in Tanah Lot Tourism Object

Sugiartawan,

Hartati*

2019

IJEAT

View full text Add to dashboard Cite

show abstract

“…Chen et al [1] made some investigation on the seasonal property of WLAN traffic based on ARIMA model. Dominguez et al [12] combined ARIMA models and wavelet tranform for time series forecasting. Tan et al [13] proposed an aggregation method combining ARIMA model and neutral network model for weekly, daily and hourly traffic flow prediction.…”

Section: Related Workmentioning

confidence: 99%

Integrating Granger Causality and Vector Auto-Regression for Traffic Prediction of Large-Scale WLANs

Zheng¹,

Zhou²,

Wu³

et al. 2016

KSII TIIS

View full text Add to dashboard Cite

Flexible large-scale WLANs are now widely deployed in crowded and highly mobile places such as campus, airport, shopping mall and company etc. But network management is hard for large-scale WLANs due to highly uneven interference and throughput among links. So the traffic is difficult to predict accurately. In the paper, through analysis of traffic in two real large-scale WLANs, Granger Causality is found in both scenarios. In combination with information entropy, it shows that the traffic prediction of target AP considering Granger Causality can be more predictable than that utilizing target AP alone, or that of considering irrelevant APs. So We develops new method-Granger Causality and Vector Auto-Regression (GCVAR), which takes APs series sharing Granger Causality based on Vector Auto-regression (VAR) into account, to predict the traffic flow in two real scenarios, thus redundant and noise introduced by multivariate time series could be removed. Experiments show that GCVAR is much more effective compared to that of traditional univariate time series (e.g. ARIMA, WARIMA). In particular, GCVAR consumes two orders of magnitude less than that caused by ARIMA/WARIMA.

show abstract

“…Wavelet transform can transform the time-frequency space of the original sequence from the spectrum analysis, and has been widely used in data prediction. Dominguez et al decomposed the Twitter traffic time-series by applying the discrete wavelet transform, fitting the appropriate ARIMA model, and then reconstructed the prediction time-series by using inverse wavelet transform [14]. The final results verify the feasibility of this method.…”

Section: Introductionmentioning

confidence: 99%

Time-Series Deep Learning Models for Reservoir Scheduling Problems Based on LSTM and Wavelet Transformation

et al. 2022

View full text Add to dashboard Cite

In 2022, as a result of the historically exceptional high temperatures that have been observed this summer in several parts of China, particularly in the province of Sichuan, residential demand for energy has increased. Up to 70% of Sichuan’s electricity comes from hydropower, thus creating a sensible and practical reservoir scheduling plan is essential to maximizing reservoir power generating efficiency. However, classical optimization, such as back propagation (BP) neural network, does not take into account the correlation of samples in time while generating reservoir scheduling rules. We proposed a prediction model based on LSTM neural network coupled with wavelet transformation (WT-LSTM) to address the problem. In order to extract the reservoir scheduling rules, this paper first gathers the scheduling operation data from the Xiluodu hydropower station and creates a dataset. Next, it uses the feature of the time-series prediction model with the realization of a complex nonlinear mapping function, time-series learning capability, and high prediction accuracy. The results demonstrate that the time-series deep learning network has high learning capability for reservoir scheduling by comparing evaluation indexes such as root mean square error (RMSE), rank-sum ratio (RSR), and Nash–Sutcliffe efficiency (NSE). The WT-LSTM network model put forward in this research offers better prediction accuracy than conventional recurrent neural networks and serves as a reference base for scheduling decisions by learning previous scheduling data to produce outflow solutions, which has some theoretical and practical benefits.

show abstract

A Wavelet-Based Method for Time Series Forecasting

Cited by 5 publications

References 12 publications

Time Series Data Prediction using Elman Recurrent Neural Network on Tourist Visits in Tanah Lot Tourism Object

Time Series Data Prediction using Elman Recurrent Neural Network on Tourist Visits in Tanah Lot Tourism Object

Integrating Granger Causality and Vector Auto-Regression for Traffic Prediction of Large-Scale WLANs

Time-Series Deep Learning Models for Reservoir Scheduling Problems Based on LSTM and Wavelet Transformation

Contact Info

Product

Resources

About