Master LLMs with our FREE course in collaboration with Activeloop & Intel Disruptor Initiative. Join now!


Demystifying Time Series Outliers: 2/4
Data Science   Latest   Machine Learning

Demystifying Time Series Outliers: 2/4

Last Updated on December 11, 2023 by Editorial Team

Author(s): Andrea Ianni

Originally published on Towards AI.

Unraveling Outliers in Soccer’s Social Media Time Series

After distributing coffee to everyone, Morelli, Zappa and I revisit what happened yesterday:

Rovella and the Rebel Data

We began with the #rovella-related tweets, a time series densely packed with outliers, and pinpointed them in a very straightforward manner, using just two basic pieces of information: the mean and standard deviation.

import pandas as pdimport numpy as nplink = ''tweets = pd.read_csv(link, sep=';', decimal=',', index_col='date', parse_dates=['date'])tweets_series = tweets['target']

Then we began to mercilessly cut them off, as if with a chainsaw.

Cutting-points work: 3 2 1… go! — Author# function DEFINITIONdef detect_outliers_zscore(ts, thres=3, points_not_to_touch=60, max_window=40, outliers_param=0.9): ''' param ts : Time series containing datetime index param thres : Threshold greater than 3 for making the outliers detection more strict param points_not_to_touch : Points you do not manipulate at the beginning of the series param max_window : Window considered for computing the local max param outliers_param : [0, 1] lower if I want to follow the outliers ''' ts_reworked = ts.copy(deep=True) outliers = [] dates = [] for i, d in zip(ts, ts.index): ts_so_far = ts[ts.index <= d] ts_so_far = ts_so_far.iloc[points_not_to_touch:] ts_so_far = ts_so_far[~ts_so_far.index.isin(dates)] length_so_far = ts_so_far.shape[0] mean = np.mean(ts_so_far) std = np.std(ts_so_far) max_so_far = np.max(ts_so_far.iloc[:-max_window]) surplus = (outliers_param * (i – max_so_far)) max_so_far_augmented =… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI

Feedback ↓