Time series forecasting of river flow using an integrated approach of wavelet multi-resolution analysis and evolutionary data-driven models. A case study: Sebaou River (Algeria)

2018 ◽  
pp. 1-17 ◽  
Author(s):  
Mousaab Zakhrouf ◽  
Hamid Bouchelkia ◽  
Madani Stamboul ◽  
Sungwon Kim ◽  
Salim Heddam
Keyword(s):  
Time Series ◽  
River Flow ◽  
Integrated Approach ◽  
Time Series Forecasting ◽  
Data Driven ◽  
Multi Resolution Analysis ◽  
Resolution Analysis

scholarly journals Prediksi Kunjungan Wisatawan Taman Nasional Gunung Merbabu dengan Time Series Forecasting dan LSTM

2020 ◽  
Vol 11 (2) ◽  
pp. 131
Author(s):  
Josua Manullang ◽  
Albertus Joko Santoso ◽  
Andi Wahju Rahardjo Emanuel
Keyword(s):  
Time Series ◽  
National Park ◽  
Short Term Memory ◽  
Lag Time ◽  
Time Series Forecasting ◽  
Mean Square ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Abstract. Prediction of tourist visits of Mount Merbabu National Park (TNGMb) needs to be done to control the number of visitors and to preserve the national park. The combination of time series forecasting (TSF) and deep learning methods has become a new alternative for prediction. This case study was conducted to implement several methods combination of TSF and Long-Short Term Memory (LSTM) to predict the visits. In this case study, there are 18 modelling scenarios as research objects to determine the best model by utilizing tourist visits data from 2013 to 2018. The results show that the model applying the lag time method can improve the model's ability to capture patterns on time series data. The error value is measured using the root mean square error (RMSE), with the smallest value of 3.7 in the LSTM architecture, using seven lags as a feature and one lag as a label.Keywords: Tourist Visit, Taman Nasional Gunung Merbabu, Prediction, Recurrent Neural Network, Long-Short Term MemoryAbstrak. Prediksi kunjungan wisatawan Taman Nasional Gunung Merbabu (TNGMb) perlu dilakukan untul pengendalian jumlah pengunjung dan menjaga kelestarian taman nasional. Gabungan metode antara time series forecasting (TSF) dan deep learning telah menjadi alternatif baru untuk melakukan prediksi. Studi kasus ini dilakukan untuk mengimplementasi gabungan dari beberapa macam metode antara TSF dan Long-Short Term Memory (LSTM) untuk memprediksi kunjungan pada TNGMb. Pada studi kasus ini, terdapat 18 skenario pemodelan sebagai objek penelitian untuk menentukan model terbaik, dengan memanfaatkan data jumlah kunjungan wisatawan di TNGMb mulai dari tahun 2013 sampai dengan tahun 2018. Hasil prediksi menunjukkan pemodelan dengan menerapkan metode lag time dapat meningkatakan kemampuan model untuk menangkap pola pada data deret waktu. Besar nilai kesalahan diukur menggunakan root mean square error (RMSE), dengan nilai terkecil sebesar 3,7 pada arsitektur LSTM, menggunakan tujuh lag sebagai feature dan satu lag sebagai label. Kata Kunci: Kunjungan Wisatawan, Taman Nasional Gunung Merbabu, Prediksi, Recurrent Neural Network, Long-Short Term Memory

Data-Driven Time Series Forecasting for Social Studies Using Spatio-Temporal Graph Neural Networks

2021 ◽  
Author(s):  
Yi-Fan Li ◽  
Bo Dong ◽  
Latifur Khan ◽  
Bhavani Thuraisingham ◽  
Patrick T. Brandt ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Social Studies ◽  
Time Series Forecasting ◽  
Data Driven ◽  
Temporal Graph ◽  
Spatio Temporal ◽  
Graph Neural Networks

scholarly journals Partial Correlation-Based Attention for Multivariate Time Series Forecasting

2020 ◽  
Vol 34 (10) ◽  
pp. 13720-13721
Author(s):  
Won Kyung Lee
Keyword(s):  
Time Series ◽  
Partial Correlation ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Time Series Forecasting ◽  
Data Driven ◽  
Series Data ◽  
Time Lags ◽  
Dependency Structure ◽  
Agnostic Learning

A multivariate time-series forecasting has great potentials in various domains. However, it is challenging to find dependency structure among the time-series variables and appropriate time-lags for each variable, which change dynamically over time. In this study, I suggest partial correlation-based attention mechanism which overcomes the shortcomings of existing pair-wise comparisons-based attention mechanisms. Moreover, I propose data-driven series-wise multi-resolution convolutional layers to represent the input time-series data for domain agnostic learning.

Time Series Forecasting in Retail Sales Using LSTM and Prophet

2021 ◽  
pp. 241-262
Author(s):  
Clony Junior ◽  
Pedro Gusmão ◽  
José Moreira ◽  
Ana Maria M. Tome
Keyword(s):  
Time Series ◽  
Data Science ◽  
Short Term Memory ◽  
Special Kind ◽  
Time Series Forecasting ◽  
Short Term ◽  
Term Memory ◽  
Use Of Time

Data science highlights fields of study and research such as time series, which, although widely explored in the past, gain new perspectives in the context of this discipline. This chapter presents two approaches to time series forecasting, long short-term memory (LSTM), a special kind of recurrent neural network (RNN), and Prophet, an open-source library developed by Facebook for time series forecasting. With a focus on developing forecasting processes by data mining or machine learning experts, LSTM uses gating mechanisms to deal with long-term dependencies, reducing the short-term memory effect inherent to the traditional RNN. On the other hand, Prophet encapsulates statistical and computational complexity to allow broad use of time series forecasting, prioritizing the expert's business knowledge through exploration and experimentation. Both approaches were applied to a retail time series. This case study comprises daily and half-hourly forecasts, and the performance of both methods was measured using the standard metrics.

Feature Selection for Time Series Forecasting: A Case Study

2008 ◽  
Author(s):  
Rubén García Pajares ◽  
Jose M. Benítez ◽  
Gregorio Sáinz Palmero
Keyword(s):  
Time Series ◽  
Feature Selection ◽  
Time Series Forecasting ◽  
Selection For

scholarly journals Conjunction of ANN and threshold based wavelet de-noising approach for forecasting suspended sediment load

2013 ◽  
Vol 3 (1) ◽  
pp. 09-25
Author(s):  
Vahid Nourani ◽  
Aida Yahyavi Rahimi ◽  
Farzad Hassan Nejad
Keyword(s):  
Time Series ◽  
Suspended Sediment ◽  
Sediment Load ◽  
Data Driven ◽  
Suspended Sediment Load ◽  
Potomac River ◽  
Denoising Method ◽  
Raw Data ◽  
Daily Streamflow

Information on suspended sediment load (SSL) is fundamental for numerous water resources management and environmental protection projects. This phenomenon has the inherent complexity due to a large number of vague parameters and existence of both spatial variability of the basin characteristics and temporal climatic patterns. This complexity turns into a barrier to get accurate prediction by conventional linear methods. On the other hand, the extent of the noise on hydrological data reduces the performance of data-driven models like Artificial Neural Networks (ANNs). Although ANNs could capture the complex nonlinear relationship between input and output parameters, being data-driven method positioned it in a state of need to preprocessed data. In this paper, the application of ANN approach focusing on wavelet- based denoising method for modeling daily streamflow-sediment relationship was proposed. The daily streamflow and SSL data observed at outlet of the Potomac River in USA were used as the case study. Achieving this purpose, Daubechies (db) was used as mother wavelet to decompose both streamflow and sediment time series into detailed and approximation subseries. Decomposition at level ten via db3 and at level eight via db5 were examined for streamflow and SSL time series, respectively. At first, the appropriate input combination with raw data to estimate current SSL was determined and re-imposed to ANN with denoised data.  The comparison of results reveals that in term of determination coefficient, the obtained result by denoised data was improved up to 23.2% with raged to use noisy, raw data and this exhibits that denoised data can be employed productively in ANN-based daily SSL forecasting.

scholarly journals A Data-Driven Surrogate Modelling Approach for Acceleration of Short-Term Simulations of a Dynamic Urban Drainage Simulator

Water ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 1849 ◽  
Author(s):  
Mahmood Mahmoodian ◽  
Jairo Arturo Torres-Matallana ◽  
Ulrich Leopold ◽  
Georges Schutz ◽  
Francois H. L. R. Clemens
Keyword(s):  
Time Series ◽  
Storage Tank ◽  
Data Driven ◽  
Urban Drainage ◽  
Rainfall Time Series ◽  
Short Term ◽  
Time Step ◽  
Speed Up ◽  
Error Indicators

In this study, applicability of a data-driven Gaussian Process Emulator (GPE) technique to develop a dynamic surrogate model for a computationally expensive urban drainage simulator is investigated. Considering rainfall time series as the main driving force is a challenge in this regard due to the high dimensionality problem. However, this problem can be less relevant when the focus is only on short-term simulations. The novelty of this research is the consideration of short-term rainfall time series as training parameters for the GPE. Rainfall intensity at each time step is counted as a separate parameter. A method to generate synthetic rainfall events for GPE training purposes is introduced as well. Here, an emulator is developed to predict the upcoming daily time series of the total wastewater volume in a storage tank and the corresponding Combined Sewer Overflow (CSO) volume. Nash-Sutcliffe Efficiency (NSE) and Volumetric Efficiency (VE) are calculated as emulation error indicators. For the case study herein, the emulator is able to speed up the simulations up to 380 times with a low accuracy cost for prediction of the total storage tank volume (medians of NSE = 0.96 and VE = 0.87). CSO events occurrence is detected in 82% of the cases, although with some considerable accuracy cost (medians of NSE = 0.76 and VE = 0.5). Applicability of the emulator for consecutive short-term simulations, based on real observed rainfall time series is also validated with a high accuracy (NSE = 0.97, VE = 0.89).

Sign in / Sign up

Export Citation Format

Share Document