Anomaly Detection model on Time Series data in Python

#datascience #anomalydetection #timeseries In this video we are going to see Anomaly detection using facebook prophet Anomaly detection identifies data points or observations that deviate from a dataset’s normal behavior. Anomalous data can indicate critical incidents or even potential opportunities For univarate time series on this dataset check this video - https://www.youtube.com/watch?v=D8CFPyi4ai4 For Multivariate time series model - https://www.youtube.com/watch?v=XZhPO043lqU For modeling holiday, special events, trends and non stationary data - https://www.youtube.com/watch?v=iuwcHhGNb8A Link to my TIme Series playlist - https://www.youtube.com/playlist?list=PL3N9eeOlCrP5cK0QRQxeJd6GrQvhAtpBK Time Series decomposition video - https://www.youtube.com/watch?v=pLHm4cvoZiY Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well