Fraud Analytics Using Machine-learning & Engineering on Big Data (FAME) for Telecom

no code implementations • 31 Oct 2023 • Sudarson Roy Pratihar, Subhadip Paul, Pranab Kumar Dash, Amartya Kumar Das

Telecom industries lose globally 46. 3 Billion USD due to fraud.

Detecting Concept Drift in the Presence of Sparsity -- A Case Study of Automated Change Risk Assessment System

no code implementations • 27 Jul 2022 • Vishwas Choudhary, Binay Gupta, Anirban Chatterjee, Subhadip Paul, Kunal Banerjee, Vijay Agneeswaran

In this work, we carry out a systematic study of the following: (i) different patterns of missing values, (ii) various statistical and ML based data imputation methods for different kinds of sparsity, (iii) several concept drift detection methods, (iv) practical analysis of the various drift detection metrics, (v) selecting the best concept drift detector given a dataset with missing values based on the different metrics.

Imputation

Drift-Adjusted And Arbitrated Ensemble Framework For Time Series Forecasting

no code implementations • 16 Mar 2020 • Anirban Chatterjee, Subhadip Paul, Uddipto Dutta, Smaranya Dey

We propose to employ a re-weighting based method to adjust the assigned weights to various forecasters in order to account for such distribution-drift.

Time Series Time Series Forecasting