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fix #243 #245

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Sep 3, 2020
Merged

fix #243 #245

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20 changes: 13 additions & 7 deletions examples/timeseries/timeseries_anomaly_detection.py
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Original file line number Diff line number Diff line change
Expand Up @@ -64,17 +64,19 @@

We will use the following data for training.
"""

df_small_noise.plot(legend=False)
fig, ax = plt.subplots()
df_small_noise.plot(legend=False, ax=ax)
plt.show()

"""
### Timeseries data with anomalies

We will use the following data for testing and see if the sudden jump up in the
data is detected as an anomaly.
"""

df_daily_jumpsup.plot(legend=False)
fig, ax = plt.subplots()
df_daily_jumpsup.plot(legend=False, ax=ax)
plt.show()

"""
## Prepare training data
Expand Down Expand Up @@ -170,6 +172,7 @@ def create_sequences(values, time_steps=TIME_STEPS):
plt.plot(history.history["loss"], label="Training Loss")
plt.plot(history.history["val_loss"], label="Validation Loss")
plt.legend()
plt.show()

"""
## Detecting anomalies
Expand Down Expand Up @@ -211,7 +214,6 @@ def create_sequences(values, time_steps=TIME_STEPS):

# Checking how the first sequence is learnt
plt.plot(x_train[0])
plt.show()
plt.plot(x_train_pred[0])
plt.show()

Expand All @@ -227,7 +229,9 @@ def normalize_test(values, mean, std):


df_test_value = (df_daily_jumpsup - training_mean) / training_std
df_test_value.plot(legend=False)
fig, ax = plt.subplots()
df_test_value.plot(legend=False, ax=ax)
plt.show()

# Create sequences from test values.
x_test = create_sequences(df_test_value.values)
Expand Down Expand Up @@ -284,5 +288,7 @@ def normalize_test(values, mean, std):
"""

df_subset = df_daily_jumpsup.iloc[anomalous_data_indices]
ax = df_daily_jumpsup.plot(legend=False)
fig, ax = plt.subplots()
df_daily_jumpsup.plot(legend=False, ax=ax)
df_subset.plot(legend=False, ax=ax, color="r")
plt.show()