An overview of time series analysis and forecasting. This talk is meant for individuals who are beginner data scientists with basic coding experience. Time series data problems are quite common and in demand. Time series analysis and models are used in areas such as finance, retail, human resources, environmental analysis, and more. The audience will be able to navigate, analyze, and model time series data, and apply these techniques to the problems of their choice.
This webinar is meant for individuals who are beginner data scientists with basic coding experience. By the end of this webinar individuals will be able to:
- Understand the basics of time series analysis and the related terminology
- Use machine learning to create time series models
- Forecast with, and validate these models
- Make interactive dashboards
There will also be an interactive exercise: A skeleton notebook and dashboard script will be provided. The data will be a financial set about stock prices.
0:00 Intro
2:03 Cross Sectional VS. Time Series
4:14 Why is Time Series Important
7:10 Creating Your Time Series Problem
10:52 Time Series Components
16:11 Decomposition Model
24:33 Autoregression
25:43 Moving Average
29:40 Stationarity and Augmented Dickey-Fuller Test
31:45 Integration - ARIMA Model
42:08 Residual Analysis
43:54 Ljung-Box Test
45:01 Aditional Questions
51:20 Autocorrelation Function
54:16 Interpretating ACF and PACF Plots
55:50 Interpreting Seasonal Orders
59:35 Conclusion
1:00:57 Q&A
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