Authors :
Dr. Chaitanya Kishore Reddy.M; G.Sravanth; P.Mounika
Volume/Issue :
Volume 8 - 2023, Issue 4 - April
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://bit.ly/3ATn1fS
DOI :
https://doi.org/10.5281/zenodo.7894396
Abstract :
Future forecast The greatest way to
accomplish the targeted marketing objectives is through
sales analysis. It would be better to advance your career
once you have the capacity to make strategic decisions in
sales forecasting. When predicting sales numbers, it is
important to remember that future product prices will
have an impact on the volume of sales in addition to past
sales data. Multivariate time series first appear to be the
best model for this problem. Since there is only ever one
price for a product at any given time in sales history,
unlike in real life where history is not always repeating.
It makes creating a multivariate time series more
challenging. However, the price is more dependent on
the expiration date for some seasonal or perishable
products. This additional data can aid in the creation of
a causal time series model that is more precise. The
proposed remedy makes use of a univariate time series
model, but includes the product's price as a factor that
systematically affects the prediction. Based on previous
sales data, the price influence is calculated using data
correlation analysis and customizable price ranges to
find products with comparable histories. This unique
strategy is simple to compute compared to other
methods and enables the pricing parameter for
simulations and forecasts to be chosen in advance
Keywords :
Sales Predicton,Forecasting, Linear Regression,Random Forest,Classification, Regression, Decision Trees, Training set.
Future forecast The greatest way to
accomplish the targeted marketing objectives is through
sales analysis. It would be better to advance your career
once you have the capacity to make strategic decisions in
sales forecasting. When predicting sales numbers, it is
important to remember that future product prices will
have an impact on the volume of sales in addition to past
sales data. Multivariate time series first appear to be the
best model for this problem. Since there is only ever one
price for a product at any given time in sales history,
unlike in real life where history is not always repeating.
It makes creating a multivariate time series more
challenging. However, the price is more dependent on
the expiration date for some seasonal or perishable
products. This additional data can aid in the creation of
a causal time series model that is more precise. The
proposed remedy makes use of a univariate time series
model, but includes the product's price as a factor that
systematically affects the prediction. Based on previous
sales data, the price influence is calculated using data
correlation analysis and customizable price ranges to
find products with comparable histories. This unique
strategy is simple to compute compared to other
methods and enables the pricing parameter for
simulations and forecasts to be chosen in advance
Keywords :
Sales Predicton,Forecasting, Linear Regression,Random Forest,Classification, Regression, Decision Trees, Training set.
