Tracking Context Changes through Meta-Learning
Machine Learning - Special issue on multistrategy learning
Machine Learning - Special issue on context sensitivity and concept drift
A streaming ensemble algorithm (SEA) for large-scale classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A perspective view and survey of meta-learning
Artificial Intelligence Review
Effective Learning in Dynamic Environments by Explicit Context Tracking
ECML '93 Proceedings of the European Conference on Machine Learning
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Systematic data selection to mine concept-drifting data streams
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Experiencing SAX: a novel symbolic representation of time series
Data Mining and Knowledge Discovery
Dynamic integration of classifiers for handling concept drift
Information Fusion
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts
The Journal of Machine Learning Research
Metalearning: Applications to Data Mining
Metalearning: Applications to Data Mining
Food Wholesales Prediction: What Is Your Baseline?
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
Towards Context Aware Food Sales Prediction
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
Learning with local drift detection
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Context mining and integration into predictive web analytics
Proceedings of the 22nd international conference on World Wide Web companion
Discovering temporal hidden contexts in web sessions for user trail prediction
Proceedings of the 22nd international conference on World Wide Web companion
A survey on concept drift adaptation
ACM Computing Surveys (CSUR)
| Hi-index | 12.05 |
Sales prediction is an essential part of stock planning for the wholesales and retail business. It is a complex task because of the large number of factors affecting the demand. Designing an intelligent predictor that would beat a simple moving average baseline across a number of products appears to be a non-trivial task. We present an intelligent two level sales prediction approach that switches the predictors depending on the properties of the historical sales. First, we learn how to categorize the sales time series into 'predictable' and 'random' based on structural, shape and relational features related to the products and the environment using meta learning approach. We introduce a set of novel meta features to capture behavior, shape and relational properties of the sales time series. Next, for the products identified as 'predictable' we apply an intelligent base predictor, while for 'random' we use a moving average. Using the real data from a food wholesales company we show how the prediction accuracy can be improved using this strategy, as compared to the baseline predictor as well as an ensemble of predictors. In our study we also show that by applying an intelligent predictor for the most 'predictable' products we can control the risk of performing worse than the baseline.