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In statistics, the demand - or the sales - of a given product is said to

The first day of the year (January 1st) is marked with a gray vertical marker. The historical data appears in red while the Lokad forecast is displayed in purple. The seasonality can be visually observed as a similarity of the patterns from one year to the next; use the gray markers as references.

`Y(t) = S(t) * Z(t)`

where `S(t + 1 year) = S(t)`

If such a function

- Compute the deseasonalized time-series as
`Z(t) = Y(t) / S(t)`

. - Produce the forecast over the time-series
*Z(t)*, possibly through moving average. - Re-apply the seasonality indices to the forecast afterward.

Back to the initial problem of estimating the seasonal indices

`S(t) = AVERAGE( Y(t-1)/MA(t-1) + Y(t-2)/MA(t-2) + Y(t-3)/MA(t-3) + ... )`

where

The approach propose in this section is

**Time-series are short.**The lifespan of most consumer goods do not exceed 3 or 4 years. As a result, for a given product, sales history offers on average very few points in the past to estimate each seasonal index (that is to say the values of*S(t)*during the course of the year, cf. the previous section).**Time-series are noisy.**Random market fluctuations impact the sales, and make the seasonality more difficult to isolate.**Multiple seasonalities are involved.**When looking at sales at the store level, the seasonality of the product itself is typically entangled with the seasonality of the store.**Other patterns**such as trend or product lifecycle**also impact time-series**, introducing various sort of bias in the estimation.

A simple - albeit manpower intensive - method to address those issues consists of manually creating

Those

The forecasting technology of Lokad natively handles both seasonality and quasi-seasonality, so you don't have to

In order to overcome issues raised by the limited historical depth available for most time-series in retail or manufacturing, Lokad uses

Get optimized sales forecasts with our inventory forecasting technology. Lokad specializes in inventory optimization through demand forecasting. Seasonality management - and much more - are native features of our forecasting engine.

- ABC analysis
- Backorders
- Container shipments
- Economic drivers
- Economic order quantity
- Fill Rate
- Financial impact of accuracy
- Inventory accuracy
- Inventory control
- Inventory costs (carrying costs)
- Inventory turnover
- Lead time
- Lead demand
- Min/Max Planning
- Minimal Order Quantities (MOQ)
- Multichannel Order Management
- Optimal service level formula
- Perpetual Inventory
- Phantom Inventory
- Prioritized Ordering
- Product life-cycle
- Quantitative supply chain
- Reorder point
- Replenishment
- Safety stock
- Service level
- Stock-keeping unit (SKU)
- Stock Reward Function

- Backtesting
- Continous Ranked Probability Score
- Data preparation
- Forecasting accuracy
- Forecasting methods
- Obfuscation
- Overfitting
- Pinball loss function
- Probabilistic forecasting
- Quantile regression
- Seasonality
- Time-series

- Bundle Pricing
- Competitive Pricing
- Cost-Plus Pricing
- Decoy Pricing
- Long-term maintenance agreement pricing
- Long-term pricing strategies
- Odd Pricing
- Penetration Pricing
- Price Elasticity of Demand
- Price Skimming
- Repricing software (Repricer)
- Styling Prices for Retail