Stock Forecasting¶

Objective¶

Stock forecasting helps production units to get an idea about raw material, pricing of goods, improvement in supply, chain management and proper control of sales.

Dataset¶

Dataset contains 4 columns as follows:-

Date - Date when product was sold
Store - Store id from where product got sold
Item - Item id
Sales - Quantity of product sold

Predict future sales of items at particular store

Prophet Time Series Modelling Workflow on Multivariate Data¶

Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends fit with yearly, weekly, daily, seasonality and holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.

Node 1 - ReadCSV¶

Reads the given CSV file : store_item_stock_train.csv

Below are the first 10 rows of data
Columns contain data as datetype, store and item which are categorical variables and sales which is a continuous variable.

Node 2 - RowFilter¶

Filters data by row with respect to store and item

Node 3 - Prophet¶

Used Facebook Prophet to create the ML model.

General Section of Prophet Model

Set Date column in DS column field
Y is the target variable. Set it to the Sales column
Set Growth as linear or logistic
We are using prophet model so it is sufficient to select seasonality in auto mode
Set mode of seasonality as additive or multiplicative
Set confidence Interval (0 to 1) which gives a range of plausible values for the parameter of interest.

Future Data section of Prophet model

FUTURE PERIOD block gives the number of steps we want to predict
FREQUENCY can be Monthly or Daily
Set INCLUDE HISTORY to true for testing the model and False for production

Node 4 - SQL¶

General Section of SQL node

Renames columns forecasted by Prophet

Schema Section of SQL node

Refreshes Schema and sets data type with respect to columns

Node 5 - JoinUsingSQL¶

General Section of JoinUsingSQL node

Joins Prediction (from SQL node) and Historical Data(from RowFilter node)

Schema Section of JoinUsingSQL node

Follow the same steps as in Schema Section of SQL node

Node 6 - SaveCSV¶

Sets path where you want to save the final output

Output Visualization¶

Graphical representation is the best way to understand insights from data. It refers to the use of charts and graphs to visually display, analyze, clarify, and interpret numerical data, functions and other qualitative structures.

Below is the workflow for Visualizing it: