Anomaly Detection With Time Series Forecasting
Language: English
Published by LAP LAMBERT Academic Publishing Okt 2020, 2020
- Softcover
- New
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
AbeBooks seller since January 23, 2017
Condition: New
£ 34.79
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Add to basketItem description from seller
This item is printed on demand - Print on Demand Titel. Neuware -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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 72 pp. Englisch.…
Seller Inventory # 9786203026160
- Title
- Anomaly Detection With Time Series Forecasting
- Author
- Hoan Dao
- Publisher
- LAP LAMBERT Academic Publishing Okt 2020
- Publication year
- 2020
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 6203026166
- ISBN 13
- 9786203026160
- Item weight
- 125 grams
- Dimensions
- 220x150x5 mm
"Synopsis" may belong to another edition of this title.
buchversandmimpf2000
Emtmannsberg, BAYE, Germany
AbeBooks seller since January 23, 2017
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