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Time Series Analysis Dashboard Plotly Visualization Temporal Data Open Source

Time Series Analysis Dashboard with Plotly for time series data visualization and temporal analysis. Complete implementation with interactive time series plots, trend analysis, seasonality detection, time series decomposition, forecasting (Prophet, ARIMA, Simple), statistical analysis, ACF/PACF analysis, lag plots, distribution analysis, outlier detection (IQR, Z-score), rolling statistics, ARIMA modeling, model evaluation (MAE, RMSE, MAPE, MASE, R²), and data preprocessing. Perfect for temporal data visualization, pattern analysis, forecasting, and interactive dashboard applications. Features 1 comprehensive Jupyter notebook and 5 Python scripts for time series analysis.

Dashboard Plotly Forecasting ARIMA Models Download Now Time Series Data Trend Analysis Get Started
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Time Series Analysis Dashboard with Plotly Project - RSK World
Time Series Analysis Dashboard with Plotly Project - RSK World
Dashboard Visualization Time Series Dashboard Python Plotly Time Series Data Temporal Analysis

This project creates comprehensive time series analysis dashboards using Plotly. It includes interactive time series plots, trend analysis, seasonality detection, time series decomposition, forecasting (Prophet, ARIMA, Simple), statistical analysis, ACF/PACF analysis, lag plots, distribution analysis, outlier detection, rolling statistics, ARIMA modeling, model evaluation, and data preprocessing. The project provides user-friendly interface for time series data visualization with 1 comprehensive Jupyter notebook and 5 Python scripts for dashboard generation. Features include interactive dashboards, temporal data analysis, pattern recognition, and comprehensive forecasting tools.

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Interactive Time Series Plots

Create beautiful, interactive time series visualizations with Plotly. Interactive zoom, pan, and hover functionality with customizable styling and multiple series support.

  • Interactive zoom and pan
  • Hover tooltips with detailed information
  • Customizable styling and colors
  • Multiple series support

Trend Analysis

Moving averages and trend detection with customizable windows. Visualize long-term patterns and identify upward or downward trends in your data.

  • Moving average calculations
  • Customizable window sizes
  • Trend detection algorithms
  • Visual trend indicators

Seasonality Detection

Identify and visualize seasonal patterns in your time series data. Box plots and seasonal decomposition to understand periodic patterns.

  • Seasonal pattern identification
  • Box plot visualizations
  • Seasonal decomposition
  • Period detection

Time Series Decomposition

Separate time series into trend, seasonal, and residual components. Additive and multiplicative decomposition methods for comprehensive analysis.

  • Trend component extraction
  • Seasonal component analysis
  • Residual analysis
  • Additive and multiplicative methods

Multiple Forecasting Methods

Three powerful forecasting approaches: Prophet (Facebook), ARIMA models, and simple trend-based forecasting. Compare different methods for best results.

  • Prophet forecasting
  • ARIMA models
  • Simple trend-based forecasting
  • Model comparison tools

Statistical Analysis

Comprehensive statistical summaries including mean, std dev, min, max, median, and count. Complete descriptive statistics for your time series data.

  • Descriptive statistics
  • Statistical summaries
  • Distribution analysis
  • Data quality metrics

ACF/PACF Analysis

Autocorrelation and partial autocorrelation function plots with confidence intervals. Essential for ARIMA model identification and time series analysis.

  • ACF plots with confidence intervals
  • PACF plots for model identification
  • Lag analysis
  • ARIMA parameter selection

Outlier Detection

Automatic detection of outliers using IQR (Interquartile Range) and Z-score methods. Customizable thresholds for identifying anomalies in your data.

  • IQR-based detection
  • Z-score method
  • Customizable thresholds
  • Anomaly visualization

Rolling Statistics

Rolling mean, standard deviation, min, and max with confidence bands. Analyze how statistics change over time with sliding windows.

  • Rolling mean and std
  • Rolling min and max
  • Confidence bands
  • Customizable window sizes

ARIMA Modeling

Fit and forecast with ARIMA(p,d,q) models. Automatic parameter selection and manual configuration options for advanced time series modeling.

  • ARIMA model fitting
  • Automatic parameter selection
  • Model diagnostics
  • Forecast generation

Model Evaluation

Comprehensive model evaluation metrics: MAE, RMSE, MAPE, MASE, and R². Compare multiple models and select the best performing one.

  • MAE, RMSE, MAPE metrics
  • MASE and R² calculations
  • Model comparison
  • Performance visualization

Data Preprocessing

Comprehensive data preprocessing tools: missing value handling (forward fill, backward fill, interpolation), outlier removal, and data transformations.

  • Missing value handling
  • Outlier removal
  • Data transformations
  • Normalization and scaling

Sample Datasets

Five comprehensive sample datasets: stock prices, sales data, temperature data, website traffic, and general sample data. Ready-to-use examples for testing.

  • Stock prices data
  • Sales data with weekly patterns
  • Temperature data with seasonality
  • Website traffic with growth trends

1 Jupyter Notebook

Comprehensive tutorial with 1 Jupyter notebook covering basic visualizations, trend analysis, seasonality, decomposition, forecasting, ARIMA modeling, and advanced analysis.

  • Step-by-step examples
  • Interactive learning
  • Code explanations
  • Best practices

Requirements

The following are the technical requirements for this project:

  • Python 3.8+
  • Plotly 5.17.0+
  • Pandas 2.0.0+
  • Statsmodels 0.14.0+
  • Prophet 1.1.5+
  • NumPy 1.24.0+
  • Jupyter Notebook
  • scikit-learn 1.3.0+
  • scipy 1.11.0+
  • kaleido 0.2.1+ (optional)

Credits & Acknowledgments

This project is developed for educational purposes and utilizes the following resources:

  • Python - PSF License
  • Plotly - MIT License
  • Statsmodels - BSD License
  • Prophet - MIT License
  • RSK World - Project Inspiration
  • GitHub Repository - Source code and documentation

Support & Contact

For paid applications, please contact us for integration help or feedback.

  • Support Email: help@rskworld.in
  • Contact Number: +91 9330539277
  • Website: RSKWORLD.in
  • GitHub Project
  • Join Our Discord
  • Slack Support Channel
  • Time Series Dashboard Documentation
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Categories

Dashboard Visualization Time Series Dashboard Python Plotly Time Series Data Temporal Analysis

Technologies

Python 3.8+
Plotly
Statsmodels
Prophet
Time Series Data

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About RSK World

Founded by Molla Samser, with Designer & Tester Rima Khatun, RSK World is your one-stop destination for free programming resources, source code, and development tools.

Founder: Molla Samser
Designer & Tester: Rima Khatun

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