https://github.com/davydantoniuk/truck-sales-timeseries-analyse
A comprehensive time series analysis of truck sales data.Implementation of forecasting models such as Holt-Winters, Polynomial Regression, and SARIMA to analyze trends, seasonality, and patterns in the dataset.
https://github.com/davydantoniuk/truck-sales-timeseries-analyse
ggplot2 holt-winters r sarima time-series
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A comprehensive time series analysis of truck sales data.Implementation of forecasting models such as Holt-Winters, Polynomial Regression, and SARIMA to analyze trends, seasonality, and patterns in the dataset.
- Host: GitHub
- URL: https://github.com/davydantoniuk/truck-sales-timeseries-analyse
- Owner: davydantoniuk
- License: mit
- Created: 2025-01-24T18:45:53.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-03-19T20:25:44.000Z (9 months ago)
- Last Synced: 2025-03-19T20:30:00.547Z (9 months ago)
- Topics: ggplot2, holt-winters, r, sarima, time-series
- Language: HTML
- Homepage: https://davydantoniuk.github.io/truck-sales-timeseries-analyse/
- Size: 563 KB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Time Series Analysis of Truck Sales Data
This repository contains a comprehensive time series analysis of truck sales data, using various statistical and machine learning models to forecast trends and patterns. The project explores Holt-Winters Exponential Smoothing, Polynomial Regression, and SARIMA models, and evaluates their performance using appropriate metrics.
## Dataset
The data used in this project is a publicly available dummy truck sales dataset, sourced from Kaggle.\
Dataset Source: [Dummy Truck Sales Data on Kaggle](https://www.kaggle.com/datasets/ddosad/dummy-truck-sales-for-time-series/data)
## Models and Methodology
The following models were implemented and analyzed:
1. Holt-Winters Exponential Smoothing
- Captures trends and seasonality in the data.
- Suitable for seasonal time series forecasting.
2. Polynomial Regression
- A machine learning approach that fits a polynomial curve to the data.
- Useful for capturing non-linear trends.
3. SARIMA (Seasonal Autoregressive Integrated Moving Average)
- A statistical model designed for seasonal and non-seasonal time series data.
- Incorporates autoregression, differencing, and moving averages.
## Model Evaluation
Each model was evaluated using the following metrics:
- R-squared (R²)
- Mean Absolute Error (MAE)
- Mean Squared Error (MSE)
- Root Mean Squared Error (RMSE)
- Mean Absolute Percentage Error (MAPE)
