Introduction to Time Series Analysis
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Section 1: Introduction to Time Series
- Introduction - What is a time series and why it matters a lot
- Panel Data vs Cross Sectional Data
- Download Resources
- Time Series Visualizations for Quick Insights
Section 2: Components of Time Series
- Components of time series.
- Difference between Seasonlity and Cyclic pattern
- Additive vs Multiplicative Time Series
- Seasonal Index Computation - Theory
- Workout Seasonal Index Computation
- Course Review
Section 3: Classical vs STL Decomposition
- Classical decomposition using Statsmodels
- STL Decomposition - video
Section 4: Detecting Anomalies
- How to spot anamoly observations in time series
Section 5: Multi Seasonal Time Series (MSTL) Decomposition
- Multi Seasonal Time Series Decomposition (MSTL)
- MSTL Decomposition Code Demo
- X13 Decomposition in Python
Section 6: Detrending Methods
- How to Detrend a Time series
- Detrending - Code Demo - video
Section 7: Deseasonalizing Time Series
- How to Deseasonalize Time Series - Approaches
- How to measure the strength of Trend and Seasonality
Section 8: ACF and PACF
- Lags and Leads
- Autocorrelation - video
- Partial Autocorrelation Function (PACF)
Section 9: Stationarity of Time Series
- What is Stationarity?
- Why even bother making a time series stationary
- How to make a time series stationary
- Statistical tests for stationarity - ADFTest - video
- KPSS Test vs ADF Test
- Missing values imputation approaches
Section 10: Dealing with Missing Values
- Missing values imputation approaches
- Customized Imputation Approaches
- How to validate missing value imputation
- Missing Values Imputation Code Dem