![icon](../../assets/method_icons/forecasting.png "icon")
# Forecasting-based methods

## Long Short-Term Memory Anomaly Detection (LSTM)

Long Short-Term Memory (LSTM) [Hochreiter and Schmidhuber 1997] network has been demonstrated to be particularly efficient in learning inner features for sub-sequences classification or time series forecasting. Such a model can also be used for anomaly detection purposes [Filonov et al. 2016, Malhotra et al. 2015]. 
The two latter papers' principle is as follows: A stacked LSTM model is trained on *normal* parts of the data. The objective is to predict the following point or the subsequence using the previous ones. Consequently, the model will be trained to forecast a healthy state of the time series, and, therefore, will fail to forecast when it will encounter an anomaly.  

The implementation of TSB-UAD corresponds to LSTM-AD [Malhotra et al. 2015].

```{eval-rst}  
.. autoclass:: TSB_UAD.models.lstm.lstm
    :members:

```

### Example

```python
import os
import numpy as np
import pandas as pd
from TSB_UAD.utils.visualisation import plotFig
from TSB_UAD.models.distance import Fourier
from TSB_UAD.models.lstm import lstm
from TSB_UAD.models.feature import Window
from TSB_UAD.utils.slidingWindows import find_length
from TSB_UAD.vus.metrics import get_metrics

#Read data
filepath = 'PATH_TO_TSB_UAD/ECG/MBA_ECG805_data.out'
df = pd.read_csv(filepath, header=None).dropna().to_numpy()
name = filepath.split('/')[-1]

data = df[:,0].astype(float)
label = df[:,1].astype(int)

#Pre-processing    
slidingWindow = find_length(data)

data_train = data[:int(0.1*len(data))]
data_test = data


#Run LSTM
modelName='LSTM'
clf = lstm(slidingwindow = slidingWindow, predict_time_steps=1, epochs = 50, patience = 5, verbose=0)
clf.fit(data_train, data_test)
measure = Fourier()
measure.detector = clf
measure.set_param()
clf.decision_function(measure=measure)

# Post-processing
score = MinMaxScaler(feature_range=(0,1)).fit_transform(score.reshape(-1,1)).ravel()
#Plot result
plotFig(data, label, score, slidingWindow, fileName=name, modelName=modelName) 

#Print accuracy
results = get_metrics(score, label, metric="all", slidingWindow=slidingWindow)
for metric in results.keys():
    print(metric, ':', results[metric])
```
```
AUC_ROC : 0.6831383664302287
AUC_PR : 0.120628447738072
Precision : 0.18439716312056736
Recall : 0.1716171617161716
F : 0.17777777777777776
Precision_at_k : 0.1716171617161716
Rprecision : 0.025
Rrecall : 0.3118637353931472
RF : 0.04628930078050358
R_AUC_ROC : 0.7300452172501921
R_AUC_PR : 0.14859987239776568
VUS_ROC : 0.7275909879072169
VUS_PR : 0.1426549169000135
Affiliation_Precision : 0.6036749472429264
Affiliation_Recall : 0.9836496679878174
```
![Result](../../assets/method_results/LSTM.png "AE Result")

### References

* [Hochreiter and Schmidhuber 1997] S. Hochreiter and J. Schmidhuber. Nov. 1997. Long short-term memory. Neural Comput., 9(8): 1735–1780.

* [Filonov et al. 2016] P. Filonov, A. Lavrentyev, and A. Vorontsov. 2016. Multivariate industrial time series with cyber-attack simulation: Fault detection using an lstm-based predictive data model. arXiv preprint arXiv:1612.06676.

* [Malhotra et al. 2015] P. Malhotra, L. Vig, G. Shroff, P. Agarwal, et al. 2015. Long short term memory networks for anomaly detection in time series. In Esann, volume 2015, p. 89.




## Concolutional Neural Network-based Anomaly Detection (CNN)

This method, called DeepAnt [Munir et al. 2019], is a forecasting-based approach that build a non-linear relationship between current and previous time series points or subsequences (using convolutional Neural Network). The outliers are detected by the deviation between the predicted and actual values.

```{eval-rst}  
.. autoclass:: TSB_UAD.models.cnn.cnn
    :members:

```

### Example

```python
import os
import numpy as np
import pandas as pd
from TSB_UAD.utils.visualisation import plotFig
from TSB_UAD.models.distance import Fourier
from TSB_UAD.models.cnn import cnn
from TSB_UAD.models.feature import Window
from TSB_UAD.utils.slidingWindows import find_length
from TSB_UAD.vus.metrics import get_metrics

#Read data
filepath = 'PATH_TO_TSB_UAD/ECG/MBA_ECG805_data.out'
df = pd.read_csv(filepath, header=None).dropna().to_numpy()
name = filepath.split('/')[-1]

data = df[:,0].astype(float)
label = df[:,1].astype(int)

#Pre-processing    
slidingWindow = find_length(data)

data_train = data[:int(0.1*len(data))]
data_test = data


#Run CNN
clf = cnn(slidingwindow = slidingWindow, predict_time_steps=1, epochs = 100, patience = 5, verbose=0)
clf.fit(data_train, data_test)
measure = Fourier()
measure.detector = clf
measure.set_param()
clf.decision_function(measure=measure)
score = clf.decision_scores_

# Post-processing
score = MinMaxScaler(feature_range=(0,1)).fit_transform(score.reshape(-1,1)).ravel()
#Plot result
plotFig(data, label, score, slidingWindow, fileName=name, modelName=modelName) 

#Print accuracy
results = get_metrics(score, label, metric="all", slidingWindow=slidingWindow)
for metric in results.keys():
    print(metric, ':', results[metric])
```
```
AUC_ROC : 0.8223519165363994
AUC_PR : 0.3990233632226723
Precision : 0.4337899543378995
Recall : 0.31353135313531355
F : 0.36398467432950193
Precision_at_k : 0.31353135313531355
Rprecision : 0.08823529411764706
Rrecall : 0.32537136066547834
RF : 0.13882386742945993
R_AUC_ROC : 0.8822285204143624
R_AUC_PR : 0.3932986259608648
VUS_ROC : 0.8811101069489891
VUS_PR : 0.4065135625548785
Affiliation_Precision : 0.9128891170124862
Affiliation_Recall : 0.9900155246934222
```
![Result](../../assets/method_results/CNN.png "CNN Result")

### References

* [Munir et al. 2019] M. Munir, S. A. Siddiqui, A. Dengel, and S. Ahmed. 2019. DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series. 7: 1991–2005. ISSN 2169-3536. DOI:10.1109/ACCESS.2018.2886457.