Long-Term Forecasting of Heterogenous Variables with Automatic Algorithm Selection

Despite the advances made in the last decade in automated planning, no planner outperforms all the others in every known benchmark domain. This observation motivates the idea of selecting different planning algorithms for different domains. Moreover, the planners' performances are affected by the structure of the search space, which depends on the encoding of the considered domain. In many domains, the performance of a planner can be improved by exploiting additional knowledge, for instance, in the form of macro-operators or entanglements. In this paper we propose ASAP, an automatic Algorithm Selection Approach for Planning that: (i) for a given domain initially learns additional knowledge, in the form of macro-operators and entanglements, which is used for creating different encodings of the given planning domain and problems, and (ii) explores the 2 dimensional space of available algorithms, defined as encodings–planners couples, and then (iii) selects the most promising algorithm for optimising either the runtimes or the quality of the solution plans.

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A Preliminary Study on Automatic Algorithm Selection for Short-Term Traffic Forecasting

Intelligent Distributed Computing XII - Studies in Computational Intelligence ◽

10.1007/978-3-319-99626-4_18 ◽

2018 ◽

pp. 204-214 ◽

Cited By ~ 3

Author(s):

Juan S. Angarita-Zapata ◽

Isaac Triguero ◽

Antonio D. Masegosa

Keyword(s):

Short Term ◽

Algorithm Selection ◽

Traffic Forecasting ◽

Preliminary Study ◽

Selection For ◽

Automatic Algorithm Selection ◽

Automatic Algorithm

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Anytime automatic algorithm selection for knapsack

Expert Systems with Applications ◽

10.1016/j.eswa.2020.113613 ◽

2020 ◽

Vol 158 ◽

pp. 113613 ◽

Cited By ~ 1

Author(s):

Isaías I. Huerta ◽

Daniel A. Neira ◽

Daniel A. Ortega ◽

Vicente Varas ◽

Julio Godoy ◽

...

Keyword(s):

Algorithm Selection ◽

Selection For ◽

Automatic Algorithm Selection ◽

Automatic Algorithm

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Improving Univariate Time Series Anomaly Detection Through Automatic Algorithm Selection and Human-in-the-Loop False-Positive Removement

The International FLAIRS Conference Proceedings ◽

10.32473/flairs.v34i1.128543 ◽

2021 ◽

Vol 34 (1) ◽

Author(s):

Cynthia Freeman ◽

Ian Beaver ◽

Abdullah Mueen

Keyword(s):

Time Series ◽

Anomaly Detection ◽

False Positive ◽

Detection Method ◽

Algorithm Selection ◽

Human In The Loop ◽

Extensive Evaluation ◽

Univariate Time Series ◽

Automatic Algorithm Selection ◽

Automatic Algorithm

The existence of a time series anomaly detection method that performs well for all domains is a myth. Given a massive library of available methods, how can one select the best method for their application? An extensive evaluation of every anomaly detection method is not feasible. Many existing anomaly detection systems do not include an avenue for human feedback, essential given the subjective nature of what even is anomalous. We present a technique for improving univariate time series anomaly detection through automatic algorithm selection and human-in-the-loop false-positive removement. These determinations were made by extensively experimenting with over 30 pre-annotated time series from the open-source Numenta Anomaly Benchmark repository. Once the highest performing anomaly detection methods are selected via these characteristics, humans can annotate the predicted outliers which are used to tune anomaly scores via subsequence similarity search and improve the selected methods for their data, increasing evaluation scores and reducing the need for annotation by 70% on predicted anomalies where annotation is used to improve F-scores.

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