scholarly journals A New Machine Learning Forecasting Algorithm Based on Bivariate Copula Functions

Forecasting ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 355-376
Author(s):  
J. A. Carrillo ◽  
M. Nieto ◽  
J. F. Velez ◽  
D. Velez
Keyword(s):  
Machine Learning ◽  
Iterative Algorithm ◽  
Machine Learning Techniques ◽  
Copula Functions ◽  
Learning Techniques ◽  
Forecasting Method ◽  
Error Terms ◽  
Bivariate Copula ◽  
New Machine

A novel forecasting method based on copula functions is proposed. It consists of an iterative algorithm in which a dependent variable is decomposed as a sum of error terms, where each one of them is estimated identifying the input variable which best “copulate” with it. The method has been tested over popular reference datasets, achieving competitive results in comparison with other well-known machine learning techniques.

scholarly journals Forecasting of Air Passengers using ARIMA Modeling

2019 ◽  
Vol 8 (11S) ◽  
pp. 1050-1054
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Autocorrelation Function ◽  
Arima Model ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Forecasting Method ◽  
Partial Autocorrelation Function ◽  
Time Series Method ◽  
Arima Modeling

Air passengers prediction is said to be the centre of gravity of the growth. With people on the move constantly, there is bound to be some dissatisfaction amongst the customers which could be due to various reason, varying from overbooking of flights to ground operations. This dissatisfaction can be controlled till a limit, in ballpark figuring. In the past, this has been done using various machine learning techniques. For this prediction, in this project, ARIMA Modeling is used which is a time series forecasting method, based on machine learning. To test the stationarity of the data, which is done using Dickey Fuller test. If the data is stationary, it is fit into the ARIMA Model. If the data isn’t stationary, it is made stationary by differencing or by logarithmic transformation. The logarithmic method to make the data stationary. Once the data is stationary, using the Partial autocorrelation function and the autocorrelation function, values of p and q are found, which are required in the time series method. These values are then fit into the ARIMA Modeling and hence, the results are predicted. Upon the use and fitting of various models, the ARIMA(2,1,2) has been the best fit, having the least RMS and RMSE values.

scholarly journals A shell dataset, for shell features extraction and recognition

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Qi Zhang ◽  
Jianhang Zhou ◽  
Jing He ◽  
Xiaodong Cun ◽  
Shaoning Zeng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Recognition Performance ◽  
Academic Research ◽  
Features Extraction ◽  
Machine Learning Techniques ◽  
New Methods ◽  
Learning Techniques ◽  
Nearest Neighbours ◽  
The World ◽  
New Machine

Abstract Shells are very common objects in the world, often used for decorations, collections, academic research, etc. With tens of thousands of species, shells are not easy to identify manually. Until now, no one has proposed the recognition of shells using machine learning techniques. We initially present a shell dataset, containing 7894 shell species with 29622 samples, where totally 59244 shell images for shell features extraction and recognition are used. Three features of shells, namely colour, shape and texture were generated from 134 shell species with 10 samples, which were then validated by two different classifiers: k-nearest neighbours (k-NN) and random forest. Since the development of conchology is mature, we believe this dataset can represent a valuable resource for automatic shell recognition. The extracted features of shells are also useful in developing and optimizing new machine learning techniques. Furthermore, we hope more researchers can present new methods to extract shell features and develop new classifiers based on this dataset, in order to improve the recognition performance of shell species.

scholarly journals Big data and black-box medical algorithms

2018 ◽  
Vol 10 (471) ◽  
pp. eaao5333 ◽  
Author(s):  
W. Nicholson Price
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Black Box ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
New Machine

New machine-learning techniques entering medicine present challenges in validation, regulation, and integration into practice.

scholarly journals Deep-Ocean Cooling May Have Offset Global Warming Until 1990

Eos ◽  
2021 ◽  
Vol 102 ◽  
Author(s):  
Robin Donovan
Keyword(s):  
Machine Learning ◽  
Global Warming ◽  
Deep Ocean ◽  
Machine Learning Techniques ◽  
New Model ◽  
Learning Techniques ◽  
New Machine

New machine learning techniques have estimated ocean temperatures below 2,000 meters, leading to a new model of warming trends.

scholarly journals DENOISING ENCODER WITH SEMANTICS AND EXCLUSION FOR SPOTTING CYBERBULLY

2020 ◽  
pp. 466-476
Keyword(s):  
Machine Learning ◽  
Representation Learning ◽  
Machine Learning Techniques ◽  
Machine Learning Method ◽  
Text Representation ◽  
Learning Techniques ◽  
Suicidal Attempts ◽  
Deep Learning Model ◽  
Better Than ◽  
New Machine

The rapid growth of social networking is supplementing the progression of cyberbullying activities. Most of the individuals involved in these activities belong to the younger generations, especially teenagers, who are at more risk of suicidal attempts. Cyberbullying is the process of using the Internet, cell phones, or other devices to send or post text or images intended to hurt or embarrass another person. Through machine learning techniques, we can detect language patterns used by bullies and their victims, and develop rules to automatically detect cyberbullying content. Here, we introduce a new machine learning method to deal with this problem. Our method named Semantic-Enhanced Marginalized Stacked Denoising Auto-Encoder (smSDA) is developed via a semantic extension of the popular deep learning model. The smSDA method detects the hidden attributes of the bullying information. Our approach experiments on two public cyberbullying corpora i.e. twitter and MySpace. The outcome of our proposed method is better than the other text representation learning methods.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

Sign in / Sign up

Export Citation Format

Share Document