Day #322 - Timeseries 101

Key Notes

• Data points indexed in time order is Time Series Order
• Observations measured over time (Regular Interval Times)

Properties of Time Series

• Level - Average value (mean) of the series
• Trend - Gradual upward or downward movements of data over time
• Seasonality - Variation that repeats itself over time (Holidays, Promos)
• Cycles - Business Cycles, Economic Cycles, etc
• Randomness - Variation that cannot be explained by trend/seasonality / caused by chance

Time Series Decomposition

• Level + Trend + Seasonality
• (Level + Trend) * Seasonality

Stationary

• All part of time series mean-variance and seasonality remain constant over time

Forecasting (Questions to Ask)

• What are you trying to predict
• Do you know how the measurements were taken
• Handling missing values (Null, Moving Average)
• Seasonality / Trend
• Shape of Data
• Assumptions being made

Feature Engineering

• Mean Every 2 Weeks
• Incremental Differences Everyday
• Mean / Variance based Features
• Rolling Window with adjusting training size

ARIMA - Auto Regression Integrated Moving Average, Hard to fine-tune
Single Exponential Smoothing, Double Exponential Smoothing, Holt-Winters Exponential Smoothing

Building Data

• Time-based values - Hour of Day
• Week based values - Week Count
• Adding Seasonality  
• Adding Promo
• Adding Averages / Means - Weekly Average, Daily Average
• Artificial X (Index from 0 to N)

Feature Generation List

• Level + Trend + Seasonality
• (Level + Trend) * Seasonality
• Mean Every 2 Weeks
• Incremental Differences Everyday
• Time-based values - Hour of Day
• Week based values - Week Count
• Adding Seasonality  
• Adding Promo
• Adding Averages / Means - Weekly Average, Daily Average

Happy Learning!!!