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AnnotatedAnomalies
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Gen_labels.m
MetricEval.m
Project_main.m
RankOneAccuracy.m
Readme
addMissingData.m
auc_pauc_fun.m
calculate_fscore.m
compute_regression.m
compute_zscore.m
conv_day_mon.m
conv_day_mon_april.m
conv_day_mon_may.m
conv_end_mon.m
find_label_bin.m
fp_fun.m
fscore_existing.m
fscore_fun.m
gen_matrix.m
getScore.m
gridsearch_params.m
house_plots.m
max_change_slope.m
merge_end_days.m
partition_data.m
periodical_day_level.m
plot_data.m
plot_data_part2.m
plot_hist.m
plot_pecan_data.m
predict_regression.m
rankone_fun.m
rankpower_fun.m
roc_fun.m
seem_paper.m
sep_month_day_end.m
split_data.m
test_validation.m
top_anomaly.m
vec_mat.m
weatherNormalisedData.m

Readme

%%This file is Copyright (C) 2018 Megha Gaur.

This repository contains the implementation details/code for anomaly detection in building's energy consumption. The reader is suggested to read through the paper to know about the approaches in detail.

For short-term data, 
Dataset: Dataport Dataset (2 months data from 10 houses)
This meter-level data is grouped into weekdays and weekends. Pecan_weekday_house_mat and Pecan_weekend_house_mat are the matlab files containing weekday and weekend data across all houses.
Code: Gen_labels.m is used to generate anomaly scores and labels depending on the user defined threshold.

For long-term data,
Dataset: HUE Dataset (3years data from 5 houses)
Code: weatherNormalisedData.m is used to annotate the year long observations.

To evaluate the performance metrics, 
Dataset: Peccan street data
Code: MetricEval.m is used to compare all the performance metrics discussed in the paper.

Another contribution of this paper is in publishing the annotated anomalies obtained from short-range and long-range datasets. 
The csv files named as Hue_House_anomalies and Dataport_House_anomalies are in the AnnotatedAnomalies folder of this repository.

Please cite our paper if you use our data or annotated anomalies as a benchmark for your research.

If you have any query feel free to contact mgaur@sfu.ca or meghag@iiitd.ac.in
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