Kaggle Days China edition was held on October 19-20 at Damei Center, Beijing.
More than 400 data scientists and enthusiasts gathered to learn, make friends, and compete in a full-day offline competition.
Kaggle Days is produced by LogicAI and Kaggle.
LogicAI is a boutique Data Science consultancy company owned by Kaggle fans and Grandmasters. As a global company, they do custom end-to-end AI and Data Science development projects as well as trainings for C-level management and tech teams.
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ARIMA(Auto Regression Integrated Moving Average) Model Implementation in Python. Following things are covered in the video:
1) Reading Time Series Data in Python using Pandas library
2) Checking for stationarity of time series model
3) Auto Arima Function to select order of Auto Regression Model
4) Predicting Future temperature values using given dataset
5) Statsmodels library is used for modelling
My medium article on the same(Contains code and dataset): https://medium.com/@nachihebbar/temperature-forecasting-with-arima-model-in-python-427b2d3bcb53?sk=ff83ea42d7ca5eee874fc4f4be219604
Recommended Books to get better at Time Series Analysis and Python:
1)Practical Time Series Analysis: https://amzn.to/31lsLhq
2)Time Series with Python: https://amzn.to/2Ez073m
3)Hands-On Time Series Analysis with R: https://amzn.to/3aUxuKq
My 2nd Youtube Channel: https://www.youtube.com/channel/UCJBz6f1QtbNrDYwR-AUcSjA
You can connect with me on linkedin: https://www.linkedin.com/in/nachiketa-hebbar-86186515b/ Video Rating: / 5
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1:51 Defining Problem
2:50 Understanding the Data
3:18 Analyzing Data (Trend, Seasonality)
4:40 Traditional Timeseries Forecasting (ARIMA, Prophet)
6:01 Univariate & Multivariate Time series
8:15 Time series with Machine Learning
9:02 Types of Time series models
11:05 Machine Learning Vs. Traditional Time Series
 Math behind Facebook prophet: https://medium.com/future-vision/the-math-of-prophet-46864fa9c55a
 Traditional time series analysis step by step: https://www.kaggle.com/freespirit08/time-series-for-beginners-with-arima
 Dealing with time series data: https://online.stat.psu.edu/stat510/lesson/1
 Catboost is slick : https://catboost.ai/docs/concepts/tutorials.html
Moving averages are foundational concepts in Time Series Analysis and form baseline models when modeling time series data. These concepts are also used in feature engineering with traditional machine learning models and as well as streaming analytics models
In this video I will be covering
Simple Moving Average
Exponential Moving Average
Weighted Moving Average
Exponential Smoothing Weighted Average