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Data Scientist: Demand Forecasting
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Module 1
Time Series Foundations and Statistical Properties
1
Time Series vs. i.i.d. Data: Why ML Assumptions Fail
Contrast time series data with cross-sectional/i.i.d. data and explain why standard ML assumptions break down for temporal data.
Time Series Decomposition: Classical & STL Methods
Decompose a time series into trend, seasonality, and residual components using classical and STL decomposition.
3
Understanding Stationarity in Time Series Analysis
Define strict and weak stationarity and explain why stationarity matters for classical forecasting methods.
4
Stationarity Tests: ADF, KPSS, and Reconciling Results
Test for stationarity using the Augmented Dickey-Fuller and KPSS tests and reconcile contradictory results.
5
Understanding ACF and PACF
Compute and interpret the autocorrelation (ACF) and partial autocorrelation (PACF) functions of a series.
Transforming Time Series Data
Apply differencing, seasonal differencing, and Box-Cox/log transformations to stabilize variance and remove trend.
Seasonality in E-commerce Demand
Identify and quantify weekly, yearly, and holiday seasonality patterns relevant to e-commerce demand.
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