Forecasting Notes

Forecasting Notes

Paper #1 - Time Series Forecasting Principles with Amazon Forecast

Types of Forecasting

• Long term - Strategic
• Short term - Operations day to day business
• Promotions - Seasonal based
• Impact of price, promotion on sales numbers

Key parameters in Retail

• Sku, Timestamp, units sold at sku level
• Sku metadata - color, department, size
• Price data - Price at that point in time
• Promotional information of sku
• Instock or purchased product

Could do at each SKU Level for sales forecast

Forecast (Target) - Units sold = (Day of week) + WeekendFlag + PromotionalFlag + IsSeasonalProduct + IsTop10SellerForseason + IsTop10inOnlinechannel + IsForAllAgegroups + IsforOld + IsforTeens + IsLowAlcholic + IsAllweatherItem + Weatherofday + ProductPriceontheDay + IsthereBundleOffer 

Additional Insights of time

‘Year’, ‘Month’, ‘Week’, ‘Day’, ‘Dayofweek’, ‘Dayofyear’, ‘Is_month_end’, ‘Is_month_start’, ‘Is_quarter_end’, ‘Is_quarter_start’, ‘Is_year_end’, and ‘Is_year_start’.

Data Insights 

• Aggregate sales by week, day, quarter, holidays, weekends

Handling Missing Data

• Zero filling
• NaN

The weighted quantile loss (wQuantileLoss) calculates how far the forecast is from actual demand in either direction as a percentage of demand on average in each quantile 

For the p10 forecast, the true value is expected to be lower than the predicted value 10% of the time

For the p90 forecast, the true value is expected to be lower than the predicted value 90% of the time

Models 

• Arima
• prophet
• DeepAR+
• Vector Autoregressive Moving Average with eXogenous regressors model

Link #2 - Time series forecasting

Forecast multiple steps:

• Single-shot: Make the predictions all at once.
• Autoregressive: Make one prediction at a time and feed the output back to the model.

Evaluation of Time Series Forecasting Models for Estimation of PM2.5 Levels in Air

More Reads

Taxonomy of Time Series Forecasting Problems

Time Series Forecasting With Deep Learning: A Survey

Keep Thinking!!!