Weekly Demand Forecast LSTM Diagram

Weekly Demand Forecast LSTM

This neural network diagram shows a small sequence model for weekly demand forecasting. The input block collects twelve weeks of sales history alongside price and promotion context. An LSTM layer processes the ordered observations so the forecast can reflect recent trends and repeating patterns. A dense layer transforms the LSTM representation before the final block estimates next-week units. The diagram gives planners and engineers a shared view of the model boundary: what history enters, where sequence learning happens, and what the model returns. It does not imply that the forecast is a purchase recommendation; inventory constraints, lead times, and service targets belong in a planning layer around the model.

類型 方塊圖標準 Ogata / standard controls textbook convention引擎 schematex-blockdiagram更新時間 2026/9/24

需求

“Create a neural network diagram for weekly demand forecasting with 12 weeks of sales, price, and promotion inputs, a 64-unit LSTM, a 32-unit ReLU layer, and a next-week unit forecast.”

接著試試Add a holiday-calendar input to the history block.Change the forecast to four weeks ahead.

這張圖裡有什麼

看懂背後的決策。

01History window

Include twelve recent weeks of demand context.

02Sequence model

Use an LSTM for ordered weekly inputs.

03Forecast horizon

State the next-week target clearly.

Use this model overview for a sequence forecast that combines past sales with commercial signals such as price and promotions.

瀏覽所有方塊圖範本 →

常見問題

What is an LSTM?

A long short-term memory network is a recurrent neural-network layer designed to retain useful information across a sequence.

Why include promotions?

Promotions can change demand sharply, so including them helps the model separate promotion effects from baseline sales.

What is a forecast horizon?

It is the future period the model predicts, such as the next week or the next four weeks.