An Entropy-Based Machine Learning Algorithm for Combining Macroeconomic Forecasts

Bretó, Carles; Espinosa, Priscila; Hernández, Penélope; Pavía, José M.

doi:10.3390/e21101015

Cited by 8 publications

(8 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To speed up the convergence, the cosine similarities between features as well as class labels are used as starting pheromone for each ant, and can be classified as a filter-based method. Various parametric entropies of decision tree algorithms are investigated by Bretó et al (2019). Partial empirical evidences were provided to support the notion that parameter adjustment of different entropy activities influences the classification.…”

Section: Related Workmentioning

confidence: 99%

Entropy based C4.5-SHO algorithm with information gain optimization in data mining

Reddy

Chittineni

2021

PeerJ Computer Science

View full text Add to dashboard Cite

Information efficiency is gaining more importance in the development as well as application sectors of information technology. Data mining is a computer-assisted process of massive data investigation that extracts meaningful information from the datasets. The mined information is used in decision-making to understand the behavior of each attribute. Therefore, a new classification algorithm is introduced in this paper to improve information management. The classical C4.5 decision tree approach is combined with the Selfish Herd Optimization (SHO) algorithm to tune the gain of given datasets. The optimal weights for the information gain will be updated based on SHO. Further, the dataset is partitioned into two classes based on quadratic entropy calculation and information gain. Decision tree gain optimization is the main aim of our proposed C4.5-SHO method. The robustness of the proposed method is evaluated on various datasets and compared with classifiers, such as ID3 and CART. The accuracy and area under the receiver operating characteristic curve parameters are estimated and compared with existing algorithms like ant colony optimization, particle swarm optimization and cuckoo search.

show abstract

Section: Related Workmentioning

confidence: 99%

Entropy based C4.5-SHO algorithm with information gain optimization in data mining

Reddy

Chittineni

2021

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…Mirna et al [ 3 ] and Guan et al [ 4 ] used the theory of entropy in predicting time series. Carles et al [ 5 ] combined entropy with machine learning to predict macroeconomics. The current tourism information discipline system is developing rapidly, and tourism information as a development basis of tourism information science has also gained great development space.…”

Section: Introductionmentioning

confidence: 99%

A Method Based on GA-CNN-LSTM for Daily Tourist Flow Prediction at Scenic Spots

Rui

Liang

et al. 2020

Entropy

View full text Add to dashboard Cite

Accurate tourist flow prediction is key to ensuring the normal operation of popular scenic spots. However, one single model cannot effectively grasp the characteristics of the data and make accurate predictions because of the strong nonlinear characteristics of daily tourist flow data. Accordingly, this study predicts daily tourist flow in Huangshan Scenic Spot in China. A prediction method (GA-CNN-LSTM) which combines convolutional neural network (CNN) and long-short-term memory network (LSTM) and optimized by genetic algorithm (GA) is established. First, network search data, meteorological data, and other data are constructed into continuous feature maps. Then, feature vectors are extracted by convolutional neural network (CNN). Finally, the feature vectors are input into long-short-term memory network (LSTM) in time series for prediction. Moreover, GA is used to scientifically select the number of neurons in the CNN-LSTM model. Data is preprocessed and normalized before prediction. The accuracy of GA-CNN-LSTM is evaluated using mean absolute percentage error (MAPE), mean absolute error (MAE), Pearson correlation coefficient and index of agreement (IA). For a fair comparison, GA-CNN-LSTM model is compared with CNN-LSTM, LSTM, CNN and the back propagation neural network (BP). The experimental results show that GA-CNN-LSTM model is approximately 8.22% higher than CNN-LSTM on the performance of MAPE.

show abstract

“…The contents cover a great diversity of topics, such as the aggregation and combination of individual forecasts [ 5 , 6 ], the comparison of forecasting performances [ 7 , 8 ], the analysis of forecasting uncertainty [ 9 ], robustness [ 10 ] and inconsistency [ 11 ], and the proposal of new forecasting approaches [ 12 , 13 , 14 ].…”

mentioning

confidence: 99%

“…Furthermore, the empiric contents are also diverse including both simulated experiments and real-world applications. More specifically, the contributions provide empirical evidence that refer to the economic growth and gross domestic product (GDP) [ 5 , 9 ], the M4 competition dataset [ 8 ], the confidence and industrial trend surveys [ 9 ], and some stock exchange composite indices (Taiwan, Shanghai, Hong-Kong) [ 11 ], as well as other real data from a Portuguese retailer [ 7 ] and a Chinese grid company [ 12 ].…”

mentioning

confidence: 99%