Advanced Time Series Topics

Take your time series analysis further with these advanced techniques:

1. Forecasting

# Simple forecasting with statsmodels or scikit-learn
from statsmodels.tsa.holtwinters import ExponentialSmoothing
model = ExponentialSmoothing(df['sales'], trend='add', seasonal='add', seasonal_periods=12)
fit = model.fit()
df['sales_forecast'] = fit.forecast(steps=12)
eda.display_timeseries(df, x='date', y=['sales', 'sales_forecast'])

2. Autocorrelation & Lag Analysis

from pandas.plotting import autocorrelation_plot, lag_plot
autocorrelation_plot(df['sales'])
lag_plot(df['sales'], lag=1)

3. Feature Engineering for Time Series

df['month'] = pd.to_datetime(df['date']).dt.month
df['year'] = pd.to_datetime(df['date']).dt.year
df['sales_lag1'] = df['sales'].shift(1)

4. Integrating Time Series Models

# Example: ARIMA model
from statsmodels.tsa.arima.model import ARIMA
arima = ARIMA(df['sales'], order=(1,1,1))
arima_fit = arima.fit()
df['arima_forecast'] = arima_fit.forecast(steps=12)

Tips for Advanced Time Series: - Always check autocorrelation before modeling - Use lag features to improve predictive models - Compare multiple forecasting models for best results - Visualize actual vs. forecasted values for validation Time Series Analysis & Visualization β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”- edaflow supports time series data exploration and visualization. Here are some practical examples:

# Line plot for time series trends
eda.display_timeseries(df, x='date', y='sales')

# Seasonal decomposition (if available)
eda.display_seasonal_decompose(df, column='sales', freq=12)

# Rolling mean and window statistics
df['sales_rolling'] = df['sales'].rolling(window=7).mean()
eda.display_timeseries(df, x='date', y='sales_rolling')

# Highlight anomalies
eda.display_timeseries(df, x='date', y='sales', highlight_anomalies=True)

Tips for Time Series: - Always plot your time series to check for trends, seasonality, and anomalies - Use rolling statistics to smooth out short-term fluctuations - Decompose series to analyze trend and seasonality components - Highlight anomalies for outlier detection and business insights

Visualization Guide

edaflow provides a rich set of visualization tools to help you understand your data, identify patterns, and communicate insights effectively. This guide covers:

  • Distribution analysis (boxplots, histograms)

  • Correlation and relationship analysis

  • Advanced scatter matrix and pair plots

  • Interactive visualizations with Plotly

Getting Started

To visualize your data, simply use edaflow’s built-in functions:

import edaflow as eda
eda.display_boxplot(df, column='age')
eda.display_histogram(df, column='income')

Correlation Analysis

Explore relationships between variables:

eda.display_correlation_matrix(df)
eda.display_scatter_matrix(df, columns=['age', 'income', 'score'])

Advanced & Interactive Plots

For publication-ready and interactive dashboards:

eda.display_interactive_boxplot(df, column='score')
eda.display_interactive_scatter(df, x='age', y='income')

More Visualization Examples

Violin Plot for Distribution and Density:

eda.display_violinplot(df, column='income', group_by='region')

Heatmap for Feature Relationships:

eda.display_heatmap(df.corr(), cmap='viridis')

Time Series Visualization:

eda.display_timeseries(df, x='date', y='sales')

Multi-Feature Scatter Plot:

eda.display_scatter(df, x='age', y='income', color='score', size='spending')

Best Practices

  • Always visualize distributions before modeling

  • Use correlation plots to detect multicollinearity

  • Leverage interactive plots for presentations and reports

  • Try different plot types to uncover hidden patterns

External Library Requirements for Advanced Features

Some advanced edaflow features require additional Python libraries. Please ensure these are installed for full functionality:

  • matplotlib: Required for all core plotting functions (boxplot, histogram, timeseries, heatmap, etc.)

  • seaborn: Required for advanced visualizations (facet grid, violinplot, heatmap, scatter matrix)

  • scikit-learn: Required for feature scaling (scale_features), machine learning utilities

  • statsmodels: Required for time series models (ARIMA, Exponential Smoothing, seasonal decomposition)

  • pandas: Required for all data manipulation and plotting

Feature Dependency Table:

To install all recommended libraries:

pip install matplotlib seaborn scikit-learn statsmodels pandas

If you encounter import errors, check that these packages are installed in your environment.

Note

Some features (e.g., PDF/SVG export) may require a working Tkinter/tcl installation for matplotlib. For headless environments, set the backend to β€˜Agg’ using:

import matplotlib
matplotlib.use('Agg')