Forecasting - Timeseries

Key Notes

• ML for forecasting
• M5 dataset hierarchical information
• A large number of correlated time series 30K

• Sparsity of data
• Weekly / Seasonal patterns
• LightGBM performed better
• Baseline exponential smoothing was better
• Benchmark with simple methods

• Table of features
• Features with info from past
• Feature / Lag1 / Lag2
• Know past/feature values
• Advertising spend of future

• Use weather forecast for future
• Create a naive forecast from the previous value
• Use lag futures

• Metadata static features

• Multi-step forecasting
• Direct forecasting
• Recursive forecasting

• Models to build one step, two steps ahead
• Recursive forecasting
• Fit once and recursively use the model with one step ahead forecast
• Append to training data
• Recreate features
• Plug that back into the model

• Independent models have issues
• Recursive is less complicated
• Correlated but errors may propagate
• Split data by time to replicate the actual forecasting process
• Split by time horizon
• Split & Forecast Horizon

• Differences for time series
• Split by time

• Feature Engineering
• Data Imputation
• Encode Categorical variable
• Temporal aspects - Time
• Future data (Marketing info / Promos)
• Do not allow data leak of future in past

• Weekly Seasonality
• Exogeneous features - Advertising spend
• Effect distributed in time
• Spend on a daily basis (Distributed lags)

• lag selection

• Seasonal trends - Festivals / Seasons

• Create a bunch of lags

• Window features (Function over a window of time)

• Rolling standard deviation
• Rolling mean
• Month trend
• biweekly trend
• Weekendtrend
• Festive trend
• Nested window features
• Model learns seasonality

• Future values are discarded while featuring computation
• Use value before the timestamp
• Expanding window of mean
• Useful libraries

Ref Slides

Exogenous Variables - having an external cause or origin

• Agriculture - Crop-eating pests, weather, crop diseases
• Supply chain - Economy, consumer attitudes changes
• Retail - Economy indicator, Weather, Unemployment rates, Inflation

Keep Forecasting!!!!