Machine learning of stochastic automata and evolutionary games

2021 ◽  
pp. 1-7
Author(s):  
Bor-Hon Lee ◽  
Albert Jing-Fuh Yang ◽  
Yenming J. Chen
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Learning Algorithm ◽  
Evolutionary Game ◽  
Evolutionary Games ◽  
Initial Distribution ◽  
Price Determination ◽  
Real Situation ◽  
Stochastic Automata ◽  
Determination Mechanism

A large categories of time series fluctuate dramatically, for example, prices of agriculture produce. Traditional methods in time series and stochastic prediction may not capture such dynamics. This paper tries to use machine learning to tune the model for a real situation by establishing a price determination mechanism on the model of stochastic automata (SA) and evolutionary game (EG). Time series volatility attributed to the chaotic process can be obtained through the learning algorithm of Markov Chain Monte Carlo (MCMC). Using machine learning through the chaotic analysis of stochastic automata and evolutionary games, we find that a more spatially aggregated distribution (smaller entropy) leads to larger time series fluctuations, regardless of the initial distribution of crops. By integrating the factors discovered in this study, we can develop a better learning algorithm in a highly volatile time series in agriculture prices.

scholarly journals UNSUPERVISED METHODOLOGY TO IN-SEASON MAPPING OF SUMMER CROPS IN URUGUAY WITH MODIS EVI’S TEMPORAL SERIES AND MACHINE LEARNING

2020 ◽  
Vol XLII-3/W12-2020 ◽  
pp. 171-176
Author(s):  
A. Cal ◽  
G. Tiscornia
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Vegetation Index ◽  
Learning Algorithm ◽  
Seasonal Pattern ◽  
Gaussian Function ◽  
Ground Truth ◽  
Agricultural Crop ◽  
Kappa Index ◽  
Nonlinear Smoothing

Abstract. This paper presents a new methodology for mapping summer crops in Uruguay, during the season, based on time-series analysis of the EVI vegetation index derived from the MODIS sensor. Time-series were processed with the k-means unsupervised machine learning algorithm. For this algorithm, the ideal number of clusters was estimated using the elbow method. Once the clusters were obtained, for each one, the average phenological signature was adjusted using a nonlinear smoothing spline regression technique. Additionally, using the derivative analysis, the key points of the curve were estimated (minimum, maximum and inflection points). When analyzing the average signature of each cluster, those whose signature follows the seasonal pattern of an agricultural crop (similar to a Gaussian function) were selected to generate a binary map of crops/non-crops. The estimated crop area is 2,336,525 hectares, higher than the official statistics of 1,667,400 hectares for the 2014–15 season. This overestimation can be explained by the resolution of the MODIS pixel (250 meters), where each has a different degree of purity; and commission errors. The methodology was validated with 5,317 ground truth points, with a general accuracy of 95.8%, kappa index of 85.6, production and user accuracy of 85.1% and 91.3% for crops/non-crops.

A Hybrid Model for PM2.5 Concentration Forecasting Based on Neighbor Structural Information, a Case in North China

2021 ◽  
Vol 13 (2) ◽  
pp. 447
Author(s):  
Ping Wang ◽  
Xuran He ◽  
Hongyinping Feng ◽  
Guisheng Zhang ◽  
Chenglu Rong
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Learning Algorithm ◽  
Structural Information ◽  
Structural Characteristics ◽  
Machine Learning Algorithm ◽  
Prediction System ◽  
Extraction Algorithm ◽  
Concentration Prediction ◽  
Pm2.5 Concentration

PM2.5 concentration prediction is an important task in atmospheric environment research, so many prediction models have been established, such as machine learning algorithm, which shows remarkable generalization ability. The time series data composed of PM2.5 concentration have the implied structural characteristics such as the sequence characteristic in time dimension and the high dimension characteristic in dynamic-mode space, which makes it different from other research data. However, when the machine learning algorithm is applied to the PM2.5 time series prediction, due to the principle of input data composition, the above structural characteristics can not be fully reflected. In our study, a neighbor structural information extraction algorithm based on dynamic decomposition is proposed to represent the structural characteristics of time series, and a new hybrid prediction system is established by using the extracted neighbor structural information to improve the accuracy of PM2.5 concentration prediction. During the process of extracting neighbor structural information, the original PM2.5 concentration series is decomposed into finite dynamic modes according to the neighborhood data, which reflects the time series structural characteristics. The hybrid model integrates the neighbor structural information in the form of input vector, which ensures the applicability of the neighbor structural information and retains the composition form the original prediction system. The experimental results of six cities show that the hybrid prediction systems integrating neighbor structural information are significantly superior to the traditional models, and also confirm that the neighbor structural information extraction algorithm can capture effective time series structural information.

scholarly journals Evaluating Temporal Correlations in Time Series Using Permutation Entropy, Ordinal Probabilities and Machine Learning

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 1025
Author(s):  
Bruno R. R. Boaretto ◽  
Roberto C. Budzinski ◽  
Kalel L. Rossi ◽  
Thiago L. Prado ◽  
Sergio R. Lopes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Analysis ◽  
Flicker Noise ◽  
Learning Algorithm ◽  
Temporal Correlation ◽  
Permutation Entropy ◽  
Series Analysis ◽  
Temporal Correlations ◽  
The Difference

Time series analysis comprises a wide repertoire of methods for extracting information from data sets. Despite great advances in time series analysis, identifying and quantifying the strength of nonlinear temporal correlations remain a challenge. We have recently proposed a new method based on training a machine learning algorithm to predict the temporal correlation parameter, α, of flicker noise (FN) time series. The algorithm is trained using as input features the probabilities of ordinal patterns computed from FN time series, xαFN(t), generated with different values of α. Then, the ordinal probabilities computed from the time series of interest, x(t), are used as input features to the trained algorithm and that returns a value, αe, that contains meaningful information about the temporal correlations present in x(t). We have also shown that the difference, Ω, of the permutation entropy (PE) of the time series of interest, x(t), and the PE of a FN time series generated with α=αe, xαeFN(t), allows the identification of the underlying determinism in x(t). Here, we apply our methodology to different datasets and analyze how αe and Ω correlate with well-known quantifiers of chaos and complexity. We also discuss the limitations for identifying determinism in highly chaotic time series and in periodic time series contaminated by noise. The open source algorithm is available on Github.

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