Multi-Step-Ahead Prediction with Long Short Term Memory Networks and Support Vector Regression

2018 ◽  
Author(s):  
Sai Li ◽  
Huajing Fang ◽  
Bing Shi
Keyword(s):  
Support Vector Regression ◽  
Short Term Memory ◽  
Support Vector ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

scholarly journals Comparative Study of Forecasting Models for Forex Predictions with the Impact of Different Currencies

2022 ◽  
Vol 12 (1) ◽  
pp. 11-22
Author(s):  
Satria Wiro Agung ◽  
◽  
Kelvin Supranata Wangkasa Rianto ◽  
Antoni Wibowo
Keyword(s):  
Machine Learning ◽  
Exchange Rates ◽  
Support Vector Regression ◽  
Short Term Memory ◽  
Political Stability ◽  
Support Vector ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Gated Recurrent Unit

- Foreign Exchange (Forex) is the exchange / trading of currencies from different countries with the aim of making profit. Exchange rates on Forex markets are always changing and it is hard to predict. Many factors affect exchange rates of certain currency pairs like inflation rates, interest rates, government debt, term of trade, political stability of certain countries, recession and many more. Uncertainty in Forex prediction can be reduced with the help of technology by using machine learning. There are many machine learning methods that can be used when predicting Forex. The methods used in this paper are Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Support Vector Regression (SVR). XGBOOST, and ARIMA. The outcome of this paper will be comparison results that show how other major currency pairs have influenced the performance and accuracy of different methods. From the results, it was proven that XGBoost outperformed other models by 0.36% compared to ARIMA model, 4.4% compared to GRU model, 8% compared to LSTM model, 9.74% compared to SVR model. Keywords— Forex Forecasting, Long Short Term Memory, Gated Recurrent Unit, Support Vector Regression, ARIMA, Extreme Gradient Boosting

scholarly journals Short-Term Load Forecasting of Microgrid via Hybrid Support Vector Regression and Long Short-Term Memory Algorithms

2020 ◽  
Vol 12 (17) ◽  
pp. 7076 ◽  
Author(s):  
Arash Moradzadeh ◽  
Sahar Zakeri ◽  
Maryam Shoaran ◽  
Behnam Mohammadi-Ivatloo ◽  
Fazel Mohammadi
Keyword(s):  
Support Vector Regression ◽  
Performance Metrics ◽  
Short Term Memory ◽  
Load Forecasting ◽  
Support Vector ◽  
Short Term ◽  
Term Memory ◽  
Short Term Load Forecasting ◽  
Long Short Term Memory ◽  
Input Variables

Short-Term Load Forecasting (STLF) is the most appropriate type of forecasting for both electricity consumers and generators. In this paper, STLF in a Microgrid (MG) is performed via the hybrid applications of machine learning. The proposed model is a modified Support Vector Regression (SVR) and Long Short-Term Memory (LSTM) called SVR-LSTM. In order to forecast the load, the proposed method is applied to the data related to a rural MG in Africa. Factors influencing the MG load, such as various household types and commercial entities, are selected as input variables and load profiles as target variables. Identifying the behavioral patterns of input variables as well as modeling their behavior in short-term periods of time are the major capabilities of the hybrid SVR-LSTM model. To present the efficiency of the suggested method, the conventional SVR and LSTM models are also applied to the used data. The results of the load forecasts by each network are evaluated using various statistical performance metrics. The obtained results show that the SVR-LSTM model with the highest correlation coefficient, i.e., 0.9901, is able to provide better results than SVR and LSTM, which have the values of 0.9770 and 0.9809, respectively. Finally, the results are compared with the results of other studies in this field, which continued to emphasize the superiority of the SVR-LSTM model.

scholarly journals Perbandingan Algoritma Long Short-Term Memory dengan SVR Pada Prediksi Harga Saham di Indonesia

Petir ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 33-43
Author(s):  
Adhib Arfan ◽  
Lussiana ETP
Keyword(s):  
Support Vector Regression ◽  
Short Term Memory ◽  
Data Preprocessing ◽  
Support Vector ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Banyak investor masih ragu dengan risiko dalam berinvestasi, hal ini disebabkan oleh fluktuasi indeks harga saham dalam waktu singkat. Telah banyak dikembangkan metode untuk memperkirakan harga saham yang akan datang namun masih memiliki keterbatasan di antaranya adalah ketergantungan jangka panjang. Tujuan penelitian yang ingin dicapai adalah menghasilkan model peramalan harga saham yang lebih efektif dan memberikan hasil yang akurat. Tahapan yang dilakukan terdiri dari pengumpulan data, preprocessing data, pembagian data, perancangan LSTM, pelatihan LSTM dan melakukan pengujian. Berdasarkan hasil pengujian, LTSM mampu memprediksi harga saham pada tahun 2017-2019 dengan performa yang baik dan tingkat kesalahan yang relatif kecil. Sedangkan pengujian menggunakan metode Support Vector Regression (SVR), LSTM memiliki nilai loss lebih baik dari algoritma SRV. Rentang data pada LSTM mempengaruhi waktu latih yang digunakan, semakin besar rentang data maka semakin lama waktu latih yang digunakan. Rentang data pada SVR mempengaruhi nilai loss, semakin besar rentang data maka semakin besar nilai loss yang dihasilkan. Dengan demikian dapat disimpulkan bahwa LSTM mampu menanggulangi ketergantungan jangka panjang dan mampu memprediksi harga saham dengan hasil yang akurat.

scholarly journals Stock Prediction using Long Short-Term Memory, Support Vector Regression and Linear Regression Algorithms

2021 ◽  
Vol 9 (VII) ◽  
pp. 3632-3638
Author(s):  
Mr. V. Manoj Kumar
Keyword(s):  
Linear Regression ◽  
Support Vector Regression ◽  
Short Term Memory ◽  
Support Vector ◽  
Stock Prediction ◽  
Short Term ◽  
Data Set ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Memory Support

Prediction is most important for stock market not only for traders but also for computer engineers who analyses stock data. We can perform this prediction by two ways one is using historical stock data and other by analyzing by information gathered from social media. It is based on model/pattern used to predict stock dataset, there are so many models are available for predicting stocks, simply model is algorithm that’s from machine learning and deep learning. In the data set the two main parameters open and close value are used for stock prediction mostly but we can also predict by its volume too. So that data is preprocessed before it is used for prediction. In this paper we used various algorithm like linear regression, support vector regression and long short-term memory for better accuracy and to compare how it different from other algorithm and for predicting future stock.

A semiautomatic annotation approach for sentiment analysis

2021 ◽  
pp. 016555152110065
Author(s):  
Rahma Alahmary ◽  
Hmood Al-Dossari
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Short Term ◽  
Term Memory ◽  
Annotation Process ◽  
Learning Classifiers ◽  
Long Short Term Memory

Sentiment analysis (SA) aims to extract users’ opinions automatically from their posts and comments. Almost all prior works have used machine learning algorithms. Recently, SA research has shown promising performance in using the deep learning approach. However, deep learning is greedy and requires large datasets to learn, so it takes more time for data annotation. In this research, we proposed a semiautomatic approach using Naïve Bayes (NB) to annotate a new dataset in order to reduce the human effort and time spent on the annotation process. We created a dataset for the purpose of training and testing the classifier by collecting Saudi dialect tweets. The dataset produced from the semiautomatic model was then used to train and test deep learning classifiers to perform Saudi dialect SA. The accuracy achieved by the NB classifier was 83%. The trained semiautomatic model was used to annotate the new dataset before it was fed into the deep learning classifiers. The three deep learning classifiers tested in this research were convolutional neural network (CNN), long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM). Support vector machine (SVM) was used as the baseline for comparison. Overall, the performance of the deep learning classifiers exceeded that of SVM. The results showed that CNN reported the highest performance. On one hand, the performance of Bi-LSTM was higher than that of LSTM and SVM, and, on the other hand, the performance of LSTM was higher than that of SVM. The proposed semiautomatic annotation approach is usable and promising to increase speed and save time and effort in the annotation process.

Categorizing Natural Language-Based Customer Satisfaction: An Implementation Method Using Support Vector Machine and Long Short-Term Memory Neural Network

2021 ◽  
Vol 13 (4) ◽  
Author(s):  
Ralph Sherwin A. Corpuz ◽  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Natural Language ◽  
Text Categorization ◽  
Short Term Memory ◽  
Support Vector ◽  
Feature Engineering ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Analyzing natural language-based Customer Satisfaction (CS) is a tedious process. This issue is practically true if one is to manually categorize large datasets. Fortunately, the advent of supervised machine learning techniques has paved the way toward the design of efficient categorization systems used for CS. This paper presents the feasibility of designing a text categorization model using two popular and robust algorithms – the Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) Neural Network, in order to automatically categorize complaints, suggestions, feedbacks, and commendations. The study found that, in terms of training accuracy, SVM has best rating of 98.63% while LSTM has best rating of 99.32%. Such results mean that both SVM and LSTM algorithms are at par with each other in terms of training accuracy, but SVM is significantly faster than LSTM by approximately 35.47s. The training performance results of both algorithms are attributed on the limitations of the dataset size, high-dimensionality of both English and Tagalog languages, and applicability of the feature engineering techniques used. Interestingly, based on the results of actual implementation, both algorithms are found to be 100% effective in accurately predicting the correct CS categories. Hence, the extent of preference between the two algorithms boils down on the available dataset and the skill in optimizing these algorithms through feature engineering techniques and in implementing them toward actual text categorization applications.

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