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...
★ ★ Code Available on GitHub ★ ★
GitHub: https://github.com/TheQuantPy
Specific Tutorial Link: https://github.com/TheQuantPy/youtube...
★ ★ QuantPy GitHub ★ ★
Collection of resources used on QuantPy YouTube channel. https://github.com/thequantpy
★ ★ Discord Community ★ ★
Join a small niche community of like-minded quants on discord. / discord
★ ★ Support our Patreon Community ★ ★
Get access to Jupyter Notebooks that can run in the browser without downloading python.
/ quantpy
★ ★ ThetaData API ★ ★
ThetaData's API provides both realtime and historical options data for end-of-day, and intraday trades and quotes. Use coupon 'QPY1' to receive 20% off on your first month.
https://www.thetadata.net/
★ ★ Online Quant Tutorials ★ ★
WEBSITE: https://quantpy.com.au
★ ★ Contact Us ★ ★
EMAIL: [email protected]
Disclaimer: All ideas, opinions, recommendations and/or forecasts, expressed or implied in this content, are for informational and educational purposes only and should not be construed as financial product advice or an inducement or instruction to invest, trade, and/or speculate in the markets. Any action or refraining from action; investments, trades, and/or speculations made in light of the ideas, opinions, and/or forecasts, expressed or implied in this content, are committed at your own risk an consequence, financial or otherwise. As an affiliate of ThetaData, QuantPy Pty Ltd is compensated for any purchases made through the link provided in this description.
Смотрите видео Statistical Analysis of Temperature Data | Time Series Analysis in Python | Weather Derivatives онлайн без регистрации, длительностью часов минут секунд в хорошем качестве. Это видео добавил пользователь QuantPy 06 Июль 2022, не забудьте поделиться им ссылкой с друзьями и знакомыми, на нашем сайте его посмотрели 17,802 раз и оно понравилось 359 людям.