Articles liés à Anomaly Detection With Time Series Forecasting

Anomaly Detection With Time Series Forecasting - Couverture souple

Dao, Hoan

 
9786203026160: Anomaly Detection With Time Series Forecasting
Couverture souple
ISBN 10 : 6203026166 ISBN 13 : 9786203026160
Editeur : International Book Market Service Ltd, 2020

Synopsis

Anomaly detection time series is a very large and complex field. In the past few years, many tech-niques based on data science were designed in order to improve the efficiency of methods developedfor this purpose. In this paper, we introduce Recurrent Neural Networks (RNNs) with LSTM units, ARIMA and Facebook Prophet library for anomaly detection with time series forcasting. Becauseof the difficulty in obtaining labeled anomaly datasets, an unsupervised technique will be experimented. Unsupervised anomaly detection is the process of detecting abnormal points in a given dataset without prior label for training. An anomaly could become normal during the data evolu-tion, therefore it is important to maintain a dynamic system to monitor the abnormality. While LSTMs and ARIMA are powerful methods for time series forecasting the future, the Prophet package works best with time series that have strong seasonal effects and several seasons of historical data. The Prophet is very powerful with missing data and shifts in the trend, and specially handles anomalies well.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Éditeur
International Book Market Service Ltd
Date d'édition
2020
Langue
anglais
ISBN 10 
6203026166
ISBN 13 
9786203026160
Reliure
Broché
Nombre de pages
72
Coordonnées du fabricant
non disponible
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