Multilayer Long Short-Term Memory (LSTM) Neural Networks in Time Series Analysis

2020 ◽  
Author(s):  
Nemanja S. Malinovic ◽  
Bratislav B. Predic ◽  
Milos Roganovic
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Time Series Analysis ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Series Analysis ◽  
Long Short Term Memory

Time series analysis and long short-term memory neural network to predict landslide displacement

Landslides ◽  
2019 ◽  
Vol 16 (4) ◽  
pp. 677-694 ◽  
Author(s):  
Beibei Yang ◽  
Kunlong Yin ◽  
Suzanne Lacasse ◽  
Zhongqiang Liu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Analysis ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Series Analysis ◽  
Long Short Term Memory

scholarly journals Currency movement forecasting using time series analysis and long short-term memory

2020 ◽  
Vol 1 (2) ◽  
pp. 71
Author(s):  
Kristina Sanjaya Putri ◽  
Siana Halim
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Foreign Exchange ◽  
Foreign Exchange Market ◽  
Short Term Memory ◽  
Window Size ◽  
Short Term ◽  
Term Memory ◽  
Series Analysis ◽  
Long Short Term Memory

Foreign exchange is one type of investment, which its goal is to minimize losses that could occur. Forecasting is a technique to minimize losses when investing. The purpose of this study is to make foreign exchange predictions using a time series analysis called Auto-Regressive Integrated Moving Average (ARIMA) and Long Short-term memory methods. This study uses the daily EUR / USD exchange rates from 2014 to March 2020. The data are used as the model to predict the value of the foreign exchange market in April 2020. The model obtained will be used for predictions in April 2020, where the RMSE values obtained from time series analysis (ARIMA) with a window size of 100 days and LSTM sequentially as follows 0.00527 and 0.00509. LSTM produces lower RMSE values than ARIMA. LSTM has better prediction results; this is because the LSTM has the ability to learn so that it can utilize a large amount of data while ARIMA cannot use it. ARIMA does not have the ability to learn even though given a large amount of data it gives poor forecasting results. The ARIMA prediction is the same as the values of the previous day.

scholarly journals Long Short-Term Memory Neural Networks for Online Disturbance Detection in Satellite Image Time Series

2018 ◽  
Vol 10 (3) ◽  
pp. 452 ◽  
Author(s):  
Yun-Long Kong ◽  
Qingqing Huang ◽  
Chengyi Wang ◽  
Jingbo Chen ◽  
Jiansheng Chen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Short Term Memory ◽  
Satellite Image ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Disturbance Detection

Deep machine learning for detection of acoustic wave reflections

2019 ◽  
Vol 19 (5) ◽  
pp. 1340-1350
Author(s):  
Mulugeta A Haile ◽  
Edward Zhu ◽  
Christopher Hsu ◽  
Natasha Bradley
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Acoustic Emission ◽  
Time Series Data ◽  
Short Term Memory ◽  
Series Data ◽  
Short Term ◽  
Term Memory ◽  
Propagating Waves ◽  
Long Short Term Memory

Acoustic emission signals are information rich and can be used to estimate the size and location of damage in structures. However, many existing algorithms may be deceived by indirectly propagated acoustic emission waves which are modulated by reflection boundaries within the structures. We propose two deep learning models to identify such waves such that existing algorithms for damage detection and localization may be used. The first approach uses long short-term memory recurrent neural networks to learn distinct patterns directly from the time-series data. In the second approach, we transform the time-series data into spectrograms and utilize convolutional neural networks to perform binary classification by leveraging spectro-temporal features. We achieved 80% classification accuracy using long short-term memory and near-perfect accuracy using convolutional neural networks on a dataset of acoustic emission signals generated by the Hsu-Nielsen sources. Both long short-term memory and convolutional neural network models were able to learn general and context-specific features of the direct and reflected acoustic emission waves. Once accurately identified, the indirectly propagating waves are filtered out while the directly propagating waves are used for source location using existing methods.

scholarly journals Stock Index Prices Prediction via Temporal Pattern Attention and Long-Short-Term Memory

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Xiaolu Wei ◽  
Binbin Lei ◽  
Hongbing Ouyang ◽  
Qiufeng Wu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Temporal Pattern ◽  
Short Term Memory ◽  
Stock Index ◽  
Support Vector ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

This study attempts to predict stock index prices using multivariate time series analysis. The study’s motivation is based on the notion that datasets of stock index prices involve weak periodic patterns, long-term and short-term information, for which traditional approaches and current neural networks such as Autoregressive models and Support Vector Machine (SVM) may fail. This study applied Temporal Pattern Attention and Long-Short-Term Memory (TPA-LSTM) for prediction to overcome the issue. The results show that stock index prices prediction through the TPA-LSTM algorithm could achieve better prediction performance over traditional deep neural networks, such as recurrent neural network (RNN), convolutional neural network (CNN), and long and short-term time series network (LSTNet).

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