Statistical Analysis of Temperature Data | Time Series Analysis in Python | Weather Derivatives
In this tutorial we further our investigation into weather derivatives by diving into some real world temperature data. The weather station data we investigate goes all the way back to Jan-1859, and we show how to group on any selection/periods using pandas dataframes to extract statistics like extreme temperatures and distributions for specific months.
The second part of this video is to complete time series analysis, specifically time series decomposition and modelling. Our first goal is to de-trend and remove seasonality using statsmodels decompose function classical decomposition using moving averages. Time series decomposition is a technique that splits a time series into several components, each representing an underlying pattern category, trend, seasonality, and noise. We discuss overfitting/underfitting and parsimony and how to use partial autocorrelation functions (PACF) and Akaike Information Criterion (AIC) to make decisions on model orders.
Online Tutorials:
1) Statistical Analysis of Temperature Data: https://quantpy.com.au/weather-deriva...
2) Time Series Decomposition and Modelling: https://quantpy.com.au/weather-deriva...
In this series we take a deep dive into a type of exotic financial products weather derivatives. Weather derivatives are financial instruments that can be used to reduce risk associated with adverse weather conditions like temperature, rainfall, frost, snow, and wind speeds.
Historical Data, Weather Observations for Sydney, Australia – Observatory Hill:
http://www.bom.gov.au/climate/data/st...
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