Time Series Forecasting with R, tidyverse and Fable packages
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Updated
May 14, 2024 - R
Time Series Forecasting with R, tidyverse and Fable packages
The objective of this project is to detect anomalies within a given dataset and assess their impact on the analysis performance. Our main focus is on developing a predictive model that will enable accurate sales forecasting. By identifying and addressing anomalies within the dataset, we aim to enhance the overall accuracy of the sales prediction
Using a linear regression method, we build a model to determine the relationship between independent and dependent variable, and then predict the sales. In the process, we will use a statistical point of view for validation.
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To predict 6 months sales using 4 years time series data of a retail store.
Description of what Sales Operations is
This depository is Homework 6 for Doing Data Science 6306 Section 401 Tuesdays at 9:30 - 11:00 PM EST, Cohort 2017 Spring semester at SMU -- "DDS-HW6" for short. Authors: Yao Yao, Jason Cessna, Steven Stevenson. This project was submitted through GitHub on RStudio version 1.0.136.
The specific problem in this project is about the time-series data trend prediction. The specific application scenario is in e-commerce. You are given a real dataset obtained from a real-world e-commerce application where there were 1000 products and 31490 customers (i.e., buyers) who bought these products. Of these 1000 products there are 100 k…
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