# Load necessary libraries
library(forecast)
library(prophet)
Project 2: Sales Forecasting for Retail
Data Science
Time Series
Forecasting
Overview
This project involved developing a time series forecasting model to predict future sales for a retail company. The objective was to optimize inventory management and improve sales strategies.
Key Features
- Data Exploration: Analyzing sales trends and seasonality.
- Model Development: Using ARIMA and Prophet models for forecasting.
- Model Evaluation: Comparing model performance using RMSE and MAPE.
R Code Snippet
# Simulate retail sales data
set.seed(123)
<- 72 # 6 years of monthly data
n_months <- seq.Date(from = as.Date("2015-01-01"), by = "month", length.out = n_months)
dates <- rpois(n_months, lambda = 200) + seq(100, 300, length.out = n_months)
sales
# Create data frame
<- data.frame(Date = dates, Sales = sales)
sales_data
# Data preprocessing
<- ts(sales_data$Sales, start = c(2015, 1), frequency = 12)
sales_ts
# ARIMA model
<- auto.arima(sales_ts)
arima_model <- forecast(arima_model, h = 12)
arima_forecast
# Prophet model
<- data.frame(ds = sales_data$Date, y = sales_data$Sales)
df <- prophet(df)
prophet_model <- make_future_dataframe(prophet_model, periods = 12, freq = "month")
future <- predict(prophet_model, future)
prophet_forecast
# Plot forecasts
plot(arima_forecast)
prophet_plot_components(prophet_model, prophet_forecast)
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