![]() ![]() Lattice’s content library features insights from industry leaders and best practices advice for all HR disciplines. To share experiences and best practices with other HR professionals, join our free Resources for Humans Slack community. We’ll continue to contribute to this story as additional resources are published. We’ve compiled all the most-helpful remote work content in one place. The Lattice team is right there with you. HR teams have been tasked with helping teams stay safe, productive, and comfortable with the transition. But while remote teams might be accustomed to working from home, the experience has been jolting to onsite employees. A non-exhaustive but growing list needs to mention: Sudeep Srivastava, Sourav Chatterjee, Jeff Handler, Rohan Bopardikar, Dawei Li, Yanjun Lin, Yang Yu, Michael Brundage, Caner Komurlu, Rakshita Nagalla, Zhichao Wang, Hechao Sun, Peng Gao, Wei Cheung, Jun Gao, Qi Wang, Morteza Kazemi, Tihamér Levendovszky, Jian Zhang, Ahmet Koylan, Kun Jiang, Aida Shoydokova, Ploy Temiyasathit, Sean Lee, Nikolay Pavlovich Laptev, Peiyi Zhang, Emre Yurtbay, Daniel Dequech, Rui Yan, William Luo, Marius Guerard, Pietari Pulkkinen, and Uttam Thakore.The coronavirus health crisis has shuttered offices and forced companies to go remote. Kats is currently maintained by Xiaodong Jiang with major contributions comingįrom many talented individuals in various forms and means. Kats is a project with several skillful researchers and engineers contributing to it. Bug fixes, code coverage improvement, etc. ![]() Support for Seasonality Removal in StatSigDetector. ![]() Standardized API for some of our legacy detectors: OutlierDetector, MKDetector.Support for meta-learning, to recommend anomaly detection algorithms and parameters for your dataset.Added new detectors: ProphetTrendDetector, Dynamic Time Warping based detectors.Improved simulators, to build synthetic data and inject anomalies.Added evaluators for anomaly/changepoint detection.Added model optimizer for anomaly/ changepoint detection.Consolidated backtesting APIs and some minor bug fixes.Added global model, a neural network forecasting model.transform( air_passengers_ts) Changelog Version 0.2.0 # calculate the TsFeatures features = TsFeatures(). # convert to TimeSeriesData object air_passengers_ts = TimeSeriesData( air_passengers_df) tsfeatures import TsFeatures # take `air_passengers` data as an example air_passengers_df = pd. # Initiate feature extraction class import pandas as pd from kats. Kats is on PyPI, so you can use pip to install it. Kats is released by Facebook's Infrastructure Data Science team. Kats aims to provide the one-stop shop for time series analysis, including detection, forecasting, feature extraction/embedding, multivariate analysis, etc. ![]() Time series analysis is an essential component of Data Science and Engineering work at industry, from understanding the key statistics and characteristics, detecting regressions and anomalies, to forecasting future trends. Kats is a toolkit to analyze time series data, a lightweight, easy-to-use, and generalizable framework to perform time series analysis. ![]()
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