Environmental Time Series Prediction with Missing Data by Machine Learning and Dynamics Recostruction

This paper discusses the prediction of time series with missing data. A novel forecast model is proposed based on max-margin classification of data with absent features. The issue of modeling incomplete time series is considered as classification of data with absent features. We employ the optimal hyperplane of classification to predict the future values. Compared with traditional predicting process of incomplete time series, our method solves the problem directly rather than fills the missing data in advance. In addition, we introduce an imputation method to estimate the missing data in the history series. Experimental results validate the effectiveness of our model in both prediction and imputation.

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Harmonic Analysis of Environmental Time Series with Missing Data or Irregular Sample Spacing

Environmental Science & Technology ◽

10.1021/es0700247 ◽

2007 ◽

Vol 41 (20) ◽

pp. 7030-7038 ◽

Cited By ~ 9

Author(s):

Shabnam Dilmaghani ◽

Isaac C. Henry ◽

Puripus Soonthornnonda ◽

Erik R. Christensen ◽

Ronald C. Henry

Keyword(s):

Time Series ◽

Missing Data ◽

Harmonic Analysis ◽

Sample Spacing ◽

Environmental Time Series

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Robust Time Series Prediction with Missing Data Based on Deep Convolutional Neural Networks

2020 International Conference on Computer Communication and Network Security (CCNS) ◽

10.1109/ccns50731.2020.00047 ◽

2020 ◽

Author(s):

Guancheng Zhou

Keyword(s):

Neural Networks ◽

Time Series ◽

Missing Data ◽

Convolutional Neural Networks ◽

Time Series Prediction ◽

Deep Convolutional Neural Networks

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Time series prediction for EMS with machine learning

2019 International Conference on Engineering, Science, and Industrial Applications (ICESI) ◽

10.1109/icesi.2019.8863006 ◽

2019 ◽

Author(s):

Marko Bizjak ◽

Gorazd Stumberger ◽

Borut Zalik ◽

Niko Lukac

Keyword(s):

Machine Learning ◽

Time Series ◽

Time Series Prediction

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An Evolutionary Framework for Nonlinear Time-Series Prediction with Adaptive Gated Mixtures of Experts

Artificial Intelligence and Integrated Intelligent Information Systems ◽

10.4018/978-1-59904-249-7.ch007 ◽

2011 ◽

pp. 114-138

Author(s):

André L.V. Coelho ◽

Clodoaldo A.M. Lima ◽

Fernando J. Von Zuben

Keyword(s):

Machine Learning ◽

Genetic Algorithm ◽

Time Series ◽

Intelligent System ◽

Time Series Prediction ◽

Integrated Approach ◽

Nonlinear Time Series ◽

Learning Problems ◽

Automatic Design ◽

Hybrid Intelligent System

A probabilistic learning technique, known as gated mixture of experts (MEs), is made more adaptive by employing a customized genetic algorithm based on the concepts of hierarchical mixed encoding and hybrid training. The objective of such effort is to promote the automatic design (i.e., structural configuration and parameter calibration) of whole gated ME instances more capable to cope with the intricacies of some difficult machine learning problems whose statistical properties are time-variant. In this chapter, we outline the main steps behind such novel hybrid intelligent system, focusing on its application to the nontrivial task of nonlinear time-series forecasting. Experiment results are reported with respect to three benchmarking time-series problems, and confirmed our expectation that the new integrated approach is capable to outperform, both in terms of accuracy and generalization, other conventional approaches, such as single neural networks and non-adaptive, handcrafted gated MEs.

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A Co-training Approach for Time Series Prediction with Missing Data

Multiple Classifier Systems - Lecture Notes in Computer Science ◽

10.1007/978-3-540-72523-7_10 ◽

2007 ◽

pp. 93-102 ◽

Cited By ~ 4

Author(s):

Tawfik A. Mohamed ◽

Neamat El Gayar ◽

Amir F. Atiya

Keyword(s):

Time Series ◽

Missing Data ◽

Time Series Prediction ◽

Training Approach

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Time series prediction using machine learning: a case of Bitcoin returns

Studies in Economics and Finance ◽

10.1108/sef-06-2021-0217 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Irfan Haider Shakri

Keyword(s):

Machine Learning ◽

Time Series ◽

Time Series Data ◽

Time Series Prediction ◽

Predictive Ability ◽

Absolute Error ◽

Data Driven ◽

Series Data ◽

Policy Uncertainty ◽

Content Type

Purpose The purpose of this study is to compare five data-driven-based ML techniques to predict the time series data of Bitcoin returns, namely, alternating model tree, random forest (RF), multiple linear regression, multi-layer perceptron regression and M5 Tree algorithms. Design/methodology/approach The data used to forecast time series data of Bitcoin returns ranges from 8 July 2010 to 30 Aug 2020. This study used several predictors to predict bitcoin returns including economic policy uncertainty, equity market volatility index, S&P returns, USD/EURO exchange rates, oil and gold prices, volatilities and returns. Five statistical indexes, namely, correlation coefficient, mean absolute error, root mean square error, relative absolute error and root relative squared error are determined. The results of these metrices are used to develop colour intensity ranking. Findings Among the machine learning (ML) techniques used in this study, RF models has shown superior predictive ability for estimating the Bitcoin returns. Originality/value This study is first of its kind to use and compare ML models in the prediction of Bitcoins. More studies can be carried out by using further cryptocurrencies and other ML data-driven models in future.

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