An Ensemble Model of Multiple Classifiers for Time Series Prediction

Chitra, A.; Uma, S.

doi:10.7763/ijcte.2010.v2.184

Cited by 33 publications

(18 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where y(n + 1) is the actual value of the time series,ŷ(n + 1) is the predicted value, and M is the number of steps that network model has to predict [50]. RMSE is used to compute the differences between the actual observation values and the predicted values which result from our model.…”

Section: Prediction Performance Measurementmentioning

confidence: 99%

Behavioural pattern identification and prediction in intelligent environments

Mahmoud

Lotfi

Langensiepen

2013

Applied Soft Computing

View full text Add to dashboard Cite

In this paper, the application of soft computing techniques in prediction of an occupant's behaviour in an inhabited intelligent environment is addressed. In this research, daily activities of elderly people who live in their own homes suffering from dementia are studied. Occupancy sensors are used to extract the movement patterns of the occupant. The occupancy data is then converted into temporal sequences of activities which are eventually used to predict the occupant behaviour. To build the prediction model, different dynamic recurrent neural networks are investigated. Recurrent neural networks have shown a great ability in finding the temporal relationships of input patterns. The experimental results show that non-linear autoregressive network with exogenous inputs model correctly extracts the long term prediction patterns of the occupant and outperformed the Elman network. The results presented here are validated using data generated from a simulator and real environments.

show abstract

Section: Prediction Performance Measurementmentioning

confidence: 99%

Behavioural pattern identification and prediction in intelligent environments

Mahmoud

Lotfi

Langensiepen

2013

Applied Soft Computing

View full text Add to dashboard Cite

show abstract

“…Due to SOM's ability to preserve topological properties and good visualization features, they perform well for the prediction of non-linear time series [17].…”

Section: Self-organizing Mapmentioning

confidence: 99%

An ensemble model for day-ahead electricity demand time series forecasting

Shen¹,

Babushkin²,

Aung³

et al. 2013

Proceedings of the Fourth International Conference on Future Energy Systems

View full text Add to dashboard Cite

In this work, we try to solve the problem of day-ahead prediction of electricity demand using an ensemble forecasting model. Based on the Pattern Sequence Similarity (PSF) algorithm, we implemented five forecasting models using different clustering techniques: K-means model (as in original PSF), Self-Organizing Map model, Hierarchical Clustering model, K-medoids model, and Fuzzy C-means model. By incorporating these five models, we then proposed an ensemble model, named Pattern Forecasting Ensemble Model (PFEM), with iterative prediction procedure. We evaluated its performance on three real-world electricity demand datasets and compared it with those of the five forecasting models individually. Experimental results show that PFEM outperforms all those five individual models in terms of Mean Error Relative and Mean Absolute Error.

show abstract

“…KNN plays an effective role as an ensemble member, once mRMR method enhances the separability by improving the interclass and intraclass distances between the data instances. KNN is also included for its effective contribution when used as an ensemble member [16]. Finally, predictions of the ensemble members are combined through majority voting which attain higher accuracy in predicting churners in telecom dataset.…”

Section: A Prediction Modelmentioning

confidence: 99%

Ensemble Based Efficient Churn Prediction Model for Telecom

Idris

Khan

2014

2014 12th International Conference on Frontiers of Information Technology

View full text Add to dashboard Cite

Predicting churners in telecom is an important application area of pattern recognition that helps in responding appropriately for retaining customers and saving the revenue loss a corporation suffers. The aim of the churn predictor is to learn the pattern of churners and thus differentiate between churners and non-churners. Handling the large dimensionality and selecting discriminative features are challenging aspects of telecom churn prediction that hinder the performance of predictors. In this study, we propose a churn prediction approach that exploits the discriminative feature selection capabilities of minimum redundancy and maximum relevance in the first step, leading to enhanced feature-label association and reduced feature set. The diverse ensemble of different base classifiers is then applied as a predictor in a second step. Final predictions are computed based on majority voting Random Forest, Rotation Forest and KNN, that ultimately leads to predicting churners from telecom datasets with higher accuracy. Simulation results are evaluated using sensitivity, specificity, area under the curve (AUC) and Q-statistic based measures on standard telecom datasets. The results indicate that our proposed approach efficiently models the challenging problem of telecom churn prediction, by effectively handling the large dimensionality and extending useful features to a diverse, majority voting based ensemble.

show abstract

An Ensemble Model of Multiple Classifiers for Time Series Prediction

Cited by 33 publications

References 12 publications

Behavioural pattern identification and prediction in intelligent environments

Behavioural pattern identification and prediction in intelligent environments

An ensemble model for day-ahead electricity demand time series forecasting

Ensemble Based Efficient Churn Prediction Model for Telecom

Contact Info

Product

Resources

About