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: quantpy.com.au...
2) Time Series Decomposition and Modelling: quantpy.com.au...
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:
www.bom.gov.au/...
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[@JohnSmith-dq6nm]

Great content. You really deserve more subscribers.

[@mattheusmegdom]

Great Video !

[@Hankhill1327]

Hi there, for the purposes of insurance wouldn’t it make more sense for someone using this as a hedge to just buy specific insurance to that business, as I’m sure the cost would be less than a call/put against a crop harvest for instance. I also wonder, how necessary is it for people trading these markets to create their own models when weather stations have been forecasting pretty accurately for many years. We could assume that news stations and research organizations have a considerable time and money investment in their own prediction models, right? Very cool video and interesting concept. I’m using an app called kalshi to trade weather markets.

[@_AbUser]

Waiting next video.. Is it possible to use somekind of harmonic functions for seasonal component representation also? How should they looks in the model?

[@QuantPy]

In short, yes it is, wait for next video

[@_AbUser]

@@QuantPy Thanks dude!

[@joeyattaway756]

Box Jenkins

[@AdamCasenas]

At 20:49, if our residuals only show a correlation for the first 15 lags, should we fit a moving average term instead?

[@amirrosli2507]

Hi, nice video. May I ask to predict weather (Temperature), is there any methods from statistical analysis besides regression . Please I need it for my project.

[@QuantPy]

You could try to use local or nonparametric models like splines.

[@amirrosli2507]

@@QuantPy thank you

[@user-ky4yg8kf5p]

for seasonal analysis, i want to take winter as DJF. in this case, if dec is in 1995, then jan and feb will be from 1996 and so on for all the other years until 2021. i have nc files, but i cant figure out how to extract data like this for winter. can anyone help me with this. i an working in python.

[@bamf6603]

Hey, are you climate data processing engineer?

[@tsehayenegash8394]

please upload in matlab or please inform me when you know the other uploader