Recurrent neural networks for time series forecasting: Current status and future directions
Abstract Recurrent Neural Networks (RNNs) have become competitive forecasting methods,
as most notably shown in the winning method of the recent M4 competition. However …
as most notably shown in the winning method of the recent M4 competition. However …
Air temperature forecasting using machine learning techniques: a review
Efforts to understand the influence of historical climate change, at global and regional levels,
have been increasing over the past decade. In particular, the estimates of air temperatures …
have been increasing over the past decade. In particular, the estimates of air temperatures …
The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation
Regression analysis makes up a large part of supervised machine learning, and consists of
the prediction of a continuous independent target from a set of other predictor variables. The …
the prediction of a continuous independent target from a set of other predictor variables. The …
[HTML][HTML] The M4 Competition: 100,000 time series and 61 forecasting methods
The M4 Competition follows on from the three previous M competitions, the purpose of which
was to learn from empirical evidence both how to improve the forecasting accuracy and how …
was to learn from empirical evidence both how to improve the forecasting accuracy and how …
Performance metrics (error measures) in machine learning regression, forecasting and prognostics: Properties and typology
A Botchkarev - arXiv preprint arXiv:1809.03006, 2018 - arxiv.org
Performance metrics (error measures) are vital components of the evaluation frameworks in
various fields. The intention of this study was to overview of a variety of performance metrics …
various fields. The intention of this study was to overview of a variety of performance metrics …
An enhanced productivity prediction model of active solar still using artificial neural network and Harris Hawks optimizer
In this paper, a new productivity prediction model of active solar still was developed
depending on improving the performance of the traditional artificial neural networks using …
depending on improving the performance of the traditional artificial neural networks using …
Forecast evaluation for data scientists: common pitfalls and best practices
Recent trends in the Machine Learning (ML) and in particular Deep Learning (DL) domains
have demonstrated that with the availability of massive amounts of time series, ML and DL …
have demonstrated that with the availability of massive amounts of time series, ML and DL …
COVID-19 vaccination awareness and aftermath: public sentiment analysis on Twitter data and vaccinated population prediction in the USA
NS Sattar, S Arifuzzaman - Applied Sciences, 2021 - mdpi.com
Social media, such as Twitter, is a source of exchanging information and opinion on global
issues such as COVID-19 pandemic. In this study, we work with a database of around 1.2 …
issues such as COVID-19 pandemic. In this study, we work with a database of around 1.2 …
Technical analysis strategy optimization using a machine learning approach in stock market indices
J Ayala, M García-Torres, JLV Noguera… - Knowledge-Based …, 2021 - Elsevier
Within the area of stock market prediction, forecasting price values or movements is one of
the most challenging issue. Because of this, the use of machine learning techniques in …
the most challenging issue. Because of this, the use of machine learning techniques in …
A new typology design of performance metrics to measure errors in machine learning regression algorithms
A Botchkarev - Interdisciplinary Journal of Information …, 2019 - informingscience.org
Aim/Purpose: The aim of this study was to analyze various performance metrics and
approaches to their classification. The main goal of the study was to develop a new typology …
approaches to their classification. The main goal of the study was to develop a new typology …