Introduction to artificial neural systems
Introduction to artificial neural systems
C4.5: programs for machine learning
C4.5: programs for machine learning
Self-organizing maps
Self-similarity and heavy tails: structural modeling of network traffic
A practical guide to heavy tails
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
On Clustering Validation Techniques
Journal of Intelligent Information Systems
Combining Classifiers with Meta Decision Trees
Machine Learning
Refining Initial Points for K-Means Clustering
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Meta-Learning by Landmarking Various Learning Algorithms
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
On the need for time series data mining benchmarks: a survey and empirical demonstration
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Characteristic-Based Clustering for Time Series Data
Data Mining and Knowledge Discovery
Cross-disciplinary perspectives on meta-learning for algorithm selection
ACM Computing Surveys (CSUR)
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
A collaborative demand forecasting process with event-based fuzzy judgements
Computers and Industrial Engineering
Feature selection for classification of oscillating time series
Expert Systems: The Journal of Knowledge Engineering
Load forecasting using a multivariate meta-learning system
Expert Systems with Applications: An International Journal
Models of performance of time series forecasters
Neurocomputing
Clustering Household Electricity Use Profiles
Proceedings of Workshop on Machine Learning for Sensory Data Analysis
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For univariate forecasting, there are various statistical models and computational algorithms available. In real-world exercises, too many choices can create difficulties in selecting the most appropriate technique, especially for users lacking sufficient knowledge of forecasting. This study focuses on rule induction for forecasting method selection by understanding the nature of historical forecasting data. A novel approach for selecting a forecasting method for univariate time series based on measurable data characteristics is presented that combines elements of data mining, meta-learning, clustering, classification and statistical measurement. We conducted a large-scale empirical study of over 300 time series using four of the most popular forecasting methods. To provide a rich portrait of the global characteristics of univariate time series, we extracted measures from a comprehensive set of features such as trend, seasonality, periodicity, serial correlation, skewness, kurtosis, nonlinearity, self-similarity, and chaos. Both supervised and unsupervised learning methods are used to learn the relationship between the characteristics of the time series and the forecasting method suitability, providing both recommendation rules, as well as visualizations in the feature space. A derived weighting schema based on the rule induction is also used to improve forecasting accuracy based on combined forecasting models.