.

It is becoming increasingly common for organizations to collect very large amounts of data over time, and to need to **detect unusual or anomalous time series.** For example, Yahoo has banks of mail servers that are monitored over time. Many measurements on servers/IoT device performances are collected every hour for each of thousands of servers in order to identify servers/devices that are behaving unusually.

Python library **tsfeature** helps to compute **a vector of features on each time series**, measuring different characteristic-features of the series. The features may include **lag correlation, the strength of seasonality, spectral entropy,** etc.

In this blog, we discuss about different feature extraction techniques from a time-series and demonstrate with two different time-series.

One of the most commonly used mechanisms of Feature Extraction mechanisms in Data Science – **Principal Component Analysis (PCA)** is also used in the context of time-series. After applying **Principal Component Analysis(Decomposition) **on the **features, various bivariate outlier detection methods** can be applied to the first two principal components. This enables the most unusual series, based on their feature vectors, to be identified. The **bivariate outlier detection** methods used are based on the highest density regions.

A change in the **variance or volatility** over time can cause problems when modeling time series with classical methods like **ARIMA**.

The **ARCH or Autoregressive Conditional Heteroskedasticity** method plays a vital role in **time-series highly volatile models **like a** stock prediction to measure the **change in **variance** that is **time-dependent**, such as i**ncreasing or decreasing volatility**.

Below, we state some of the **time-series features, functionality, and their description.**

The following code snippet shows how we can** extract relevant features with one line of code **for each feature.

`tsf_hp = tf.holt_parameters(df2['# Direct_1'].values) `

print(tsf_hp)

tsf_centrpy = tf.count_entropy(df2['# Direct_1'].values)

print(tsf_centrpy)

tsf_crossing_points =tf.crossing_points(df2['# Direct_1'].values)

print(tsf_centrpy)

tsf_entropy =tf.entropy(df2['# Direct_1'].values)

print(tsf_entropy)

tsf_flat_spots =tf.flat_spots(df2['# Direct_1'].values)

print(tsf_flat_spots)

tsf_frequency =tf.frequency(df2['# Direct_1'].values)

print(tsf_frequency)

tsf_heterogeneity = tf.heterogeneity(df2['# Direct_1'].values)

print(tsf_heterogeneity)

tsf_guerrero =tf.guerrero(df2['# Direct_1'].values)

print(tsf_guerrero)

tsf_hurst = tf.hurst(df2['# Direct_1'].values)

print(tsf_hurst)

tsf_hw_parameters = tf.hw_parameters(df2['# Direct_1'].values)

print(tsf_hw_parameters)

tsf_intv = tf.intervals(df2['# Direct_1'].values)

print(tsf_intv)

tsf_lmp = tf.lumpiness(df2['# Direct_1'].values) print(tsf_lmp)

tsf_acf = tf.acf_features(df2['# Direct_1'].values)

print(tsf_acf)

tsf_arch_stat = tf.arch_stat(df2['# Direct_1'].values)

print(tsf_arch_stat)

tsf_pacf = tf.pacf_features(df2['# Direct_1'].values)

print(tsf_pacf)

tsf_sparsity = tf.sparsity(df2['# Direct_1'].values)

print(tsf_sparsity)

tsf_stability = tf.stability(df2['# Direct_1'].values)

print(tsf_stability)

tsf_stl_features = tf.stl_features(df2['# Direct_1'].values)

print(tsf_stl_features)

tsf_unitroot_kpss = tf.unitroot_kpss(df2['# Direct_1'].values)

print(tsf_unitroot_kpss)

tsf_unitroot_pp = tf.unitroot_pp(df2['# Direct_1'].values)

print(tsf_unitroot_pp)

The results section illustrates **the values of extracted features** from **Fetal ECG.**

The below figure illustrates a time series of data collected **from Fetal ECG from where features have been extracted**.

`{'alpha': 0.9998016430979507, 'beta': 0.5262228301908355} {'count_entropy': 1.783469256071135} {'crossing_points': 436} {'entropy': 0.6493414196542769} {'flat_spots': 131} {'frequency': 1} {'arch_acf': 0.3347171050143251, 'garch_acf': 0.3347171050143251, 'arch_r2': 0.14089508110660665, 'garch_r2': 0.14089508110660665} {'hurst': 0.4931972012451876} {'hw_alpha': nan, 'hw_beta': nan, 'hw_gamma': nan} {'intervals_mean': 2516.801557547009, 'intervals_sd': nan} {'guerrero': nan} {'lumpiness': 0.01205944072461473} {'x_acf1': 0.8262122472240574, 'x_acf10': 3.079891123506255, 'diff1_acf1': -0.27648384824011435, 'diff1_acf10': 0.08236265771293629, 'diff2_acf1': -0.5980110240921641, 'diff2_acf10': 0.3724461872893135} {'arch_lm': 0.7064704126082555} {'x_pacf5': 0.7303549429779813, 'diff1x_pacf5': 0.09311680507880443, 'diff2x_pacf5': 0.7105000333917864} {'sparsity': 0.0} {'stability': 0.16986190432765097} {'nperiods': 0, 'seasonal_period': 1, 'trend': nan, 'spike': nan, 'linearity': nan, 'curvature': nan, 'e_acf1': nan, 'e_acf10': nan} {'unitroot_kpss': 0.06485903737928193} {'unitroot_pp': -908.3309773009415}`

The results section illustrates **the values of extracted features** for date wise temperature variation.

`{'alpha': 0.4387345064923509, 'beta': 0.0} {'count_entropy': -101348.71338310161} {'crossing_points': 706} {'entropy': 0.5089893350876903} {'flat_spots': 10} {'frequency': 1} {'arch_acf': 0.016273743642920828, 'garch_acf': 0.016273743642920828, 'arch_r2': 0.015091960217949008, 'garch_r2': 0.015091960217949008} {'hurst': 0.5716257806690483} {'hw_alpha': nan, 'hw_beta': nan, 'hw_gamma': nan} {'intervals_mean': 1216.0, 'intervals_sd': 1299.2740280633643} {'guerrero': nan} {'lumpiness': 5.464398615083545e-05} {'x_acf1': -0.0005483958183129098, 'x_acf10': 3.0147995912148108e-06, 'diff1_acf1': -0.5, 'diff1_acf10': 0.25, 'diff2_acf1': -0.6666666666666666, 'diff2_acf10': 0.4722222222222222} {'arch_lm': 3.6528279285796827e-06} {'nonlinearity': 0.0} {'x_pacf5': 1.5086491342316237e-06, 'diff1x_pacf5': 0.49138888888888893, 'diff2x_pacf5': 1.04718820861678} {'sparsity': 0.0} {'stability': 5.464398615083545e-05} {'nperiods': 0, 'seasonal_period': 1, 'trend': nan, 'spike': nan, 'linearity': nan, 'curvature': nan, 'e_acf1': nan, 'e_acf10': nan} {'unitroot_kpss': 0.29884876591708787} {'unitroot_pp': -3643.7791982866393}`

` `

- In this blog, we discuss easy steps to extract features from time series (both time-series have seasonality =1), which can help us in discovering anomalies.
- It is evident from the computed metrics that the first series is more stable (higher value as given by the
**stability and entropy factor**) as the time-stamped data is for a longer period with relatively few fluctuations compared to its entire period. - The second time series exhibits
**higher fluctuations**as demonstrated by a high number of crossing points. - Consequently, we also observe that the second time-series also has a lower
**lumpiness**and**intervals mean**, signifying a lower variance of variance. - unirooot_kpss and uniroot_pp reveal the existence of a unit root in the vector which in both the time-series is less than 1 and negative respectively.
**tsfeature**also supports evaluation of custom functions that come as a NumPy array as input and returns a dictionary with the feature name as a key and its value

- https://github.com/FedericoGarza/tsfeatures
- https://htmlpreview.github.io/?
- https://github.com/robjhyndman/M4metalearning/blob/master/docs/M4_m...
- project.org/web/packages/tsfeatures/tsfeatures.pdf
- https://robjhyndman.com/papers/icdm2015.pdf
- https://math.berkeley.edu/~btw/thesis4.pdf
- https://machinelearningmastery.com/develop-arch-and-garch-models-fo...
- https://ir.nctu.edu.tw/bitstream/11536/14555/1/A1997YD78100005.pdf
- Principal Component Analysis for Time Series and Other Non-Independent Data – https://link.springer.com/chapter/10.1007%2F0-387-22440-8_12

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