A deep granular network with adaptive unequal-length granulation strategy for long-term time series forecasting and its industrial applications

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.

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Gaussian process for long-term time-series forecasting

2009 International Joint Conference on Neural Networks ◽

10.1109/ijcnn.2009.5178729 ◽

2009 ◽

Cited By ~ 7

Author(s):

Weizhong Yan ◽

Hai Qiu ◽

Ya Xue

Keyword(s):

Time Series ◽

Gaussian Process ◽

Time Series Forecasting

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Building trend fuzzy granulation based LSTM recurrent neural network for long-term time series forecasting

IEEE Transactions on Fuzzy Systems ◽

10.1109/tfuzz.2021.3062723 ◽

2021 ◽

pp. 1-1

Author(s):

Yuqing Tang ◽

Fusheng Yu ◽

Witold Pedrycz ◽

Xiyang Yang ◽

Jiayin Wang ◽

...

Keyword(s):

Neural Network ◽

Time Series ◽

Recurrent Neural Network ◽

Time Series Forecasting

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A Radial Basis Function Neural Network-Based Coevolutionary Algorithm for Short-Term to Long-Term Time Series Forecasting

Studies in Computational Intelligence - Computational Intelligence ◽

10.1007/978-3-319-23392-5_7 ◽

2015 ◽

pp. 121-136

Author(s):

E. Parras-Gutierrez ◽

V. M. Rivas ◽

J. J. Merelo

Keyword(s):

Neural Network ◽

Time Series ◽

Radial Basis Function ◽

Basis Function ◽

Time Series Forecasting ◽

Short Term ◽

Coevolutionary Algorithm ◽

Radial Basis

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A Competitive Modular Neural Network for Long-Term Time Series Forecasting

Nature-Inspired Design of Hybrid Intelligent Systems - Studies in Computational Intelligence ◽

10.1007/978-3-319-47054-2_16 ◽

2016 ◽

pp. 243-254 ◽

Cited By ~ 1

Author(s):

Eduardo Méndez ◽

Omar Lugo ◽

Patricia Melin

Keyword(s):

Neural Network ◽

Time Series ◽

Time Series Forecasting ◽

Modular Neural Network

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Deep Learning for Downhole Data Prediction: A Cost-Effective Data Telemetry Through Data Analytics

10.2118/200870-ms ◽

2021 ◽

Author(s):

Bharat Thakur ◽

Robello Samuel

Keyword(s):

Time Series ◽

Deep Learning ◽

Real Time ◽

High Speed ◽

Cost Effective ◽

Time Series Forecasting ◽

Data Prediction ◽

Surface Data ◽

Median Error

Abstract Accurate real-time downhole data collection provides a better understanding of downhole dynamics and formation characteristics, which can improve wellbore placement and increase drilling efficiency by improving the rate of penetration (ROP) and reducing downtime caused by tool failure. High-speed telemetry through wired drill string has enabled real-time data acquisition, but there are significant additional costs associated with the technology. Data-driven techniques using recursive neural networks (RNN) have proven very efficient and accurate in time-series forecasting problems. In this study, we propose deep learning as a cost-effective method to predict downhole data using surface data. Downhole drilling data is a function of surface drilling parameters and downhole conditions. The downhole data acquired using relatively inexpensive methods usually have a considerable lag time depending on the signal travel length. So, the first step in the proposed method is syncing the downhole data and surface data. After the data are synced, they are then fed into an RNN-based long-term short memory (LSTM) network, which learns the relationship between the surface parameters and downhole data. LSTM networks can learn long-term relationships in the data, thus making them ideal for time-series forecasting applications. The trained model is then used to make predictions for downhole data using the given surface data. The median error for the prediction of downhole data using surface data was as low as 3% in this study. The study suggests that the developed model can accurately predict downhole data in real-time. The model is also very robust to the amount of noise or outliers present in the data and can predict downhole conditions 50–60 ft ahead with reasonable accuracy. It was observed that the prediction accuracy varied from well to well and drilling depths. The results demonstrate how deep learning can be cost-effectively employed for downhole data prediction. This paper presents a novel method for using surface data to predict downhole data by employing deep learning. The method can be deployed in real-time to aid in wellbore placement and improve drilling performance.

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