Time Series Analysis Tool

Interactive application for analyzing and forecasting temporal data patterns

Data Science Project

About the Time Series Tool

This powerful application provides comprehensive time series analysis capabilities, from decomposition and stationarity checks to automatic ARIMA modeling and forecasting. Built with Python's statsmodels and pmdarima, it offers professional-grade time series analysis in an accessible interface.

  • Interactive time series visualization with date range controls
  • Seasonal decomposition (trend, seasonal, residual)
  • Stationarity checks with Augmented Dickey-Fuller test
  • Automatic ARIMA model selection
  • Forecasting with multiple error metrics
View on HuggingFace
Time Series Analysis Demo
Features

Comprehensive Time Series Analysis

Interactive Visualization

Dynamic time series plots with customizable date ranges and yearly averaging options for better trend analysis.

Advanced Diagnostics

Complete decomposition analysis, stationarity testing, and differencing options to prepare your data for modeling.

Auto ARIMA

Automatic selection of optimal ARIMA parameters (p,d,q) using the pmdarima library's stepwise algorithm.

Interactive Demo

Try the Time Series Analysis Tool

The application is embedded below. Upload your time series data (CSV, Excel, or text file) and explore the comprehensive analysis capabilities.

How to Use

  1. Upload your time series dataset
  2. Select the time column and target variable
  3. Explore the interactive time series plot
  4. Use the "Advanced Analytics" section for decomposition and stationarity checks
  5. View automatically generated forecasts with error metrics
  6. Toggle yearly averaging for higher-level trend analysis
Technical Implementation

Under the Hood

Time Series Analysis

Statsmodels for decomposition, stationarity testing (ADF), and ACF/PACF analysis to understand temporal patterns.

ARIMA Modeling

pmdarima's auto_arima for automatic parameter selection and optimal model fitting.

Visualization

Plotly for interactive visualizations and matplotlib for statistical plots (ACF/PACF).

XANE Assistant