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Zhongwei Li
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skills/time-series-forecaster/assets/visualization_template.py
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238
skills/time-series-forecaster/assets/visualization_template.py
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#!/usr/bin/env python3
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"""
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visualization_template.py
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This module provides functions for visualizing time series data and forecasts.
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It includes functionalities for plotting historical data, forecasts,
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confidence intervals, and residuals.
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Example:
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To use this module, import it and call the desired visualization functions.
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For example:
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>>> import visualization_template as vt
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>>> import pandas as pd
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>>> import matplotlib.pyplot as plt
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>>> # Assume 'data' is a pandas DataFrame with a 'time' column and a 'value' column
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>>> # Assume 'forecast' is a pandas DataFrame with a 'time' column and a 'forecast' column
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>>> vt.plot_time_series(data, forecast, title="Time Series Forecast")
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>>> plt.show()
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"""
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import pandas as pd
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import matplotlib.pyplot as plt
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import matplotlib.dates as mdates
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import numpy as np
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from typing import Optional, Tuple, List
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def plot_time_series(
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data: pd.DataFrame,
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forecast: Optional[pd.DataFrame] = None,
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confidence_interval: Optional[Tuple[float, float]] = None,
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title: str = "Time Series Data and Forecast",
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xlabel: str = "Time",
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ylabel: str = "Value",
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figsize: Tuple[int, int] = (12, 6),
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data_color: str = 'blue',
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forecast_color: str = 'red',
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confidence_color: str = 'lightgray'
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) -> None:
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"""
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Plots the time series data and, optionally, a forecast with confidence intervals.
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Args:
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data (pd.DataFrame): DataFrame containing the time series data.
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Must have a column named 'time' and a column with the time series values.
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forecast (pd.DataFrame, optional): DataFrame containing the forecast. Defaults to None.
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Must have a column named 'time' and a column with the forecast values (e.g., 'forecast').
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confidence_interval (Tuple[float, float], optional): Tuple containing the lower and upper bounds of the confidence interval. Defaults to None.
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If provided, `forecast` DataFrame must have columns for lower and upper bounds (e.g., 'lower', 'upper').
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title (str, optional): Title of the plot. Defaults to "Time Series Data and Forecast".
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xlabel (str, optional): Label for the x-axis. Defaults to "Time".
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ylabel (str, optional): Label for the y-axis. Defaults to "Value".
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figsize (Tuple[int, int], optional): Figure size (width, height). Defaults to (12, 6).
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data_color (str, optional): Color of the historical data line. Defaults to 'blue'.
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forecast_color (str, optional): Color of the forecast line. Defaults to 'red'.
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confidence_color (str, optional): Color of the confidence interval shading. Defaults to 'lightgray'.
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Raises:
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ValueError: If `data` does not have a 'time' column.
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ValueError: If `data` does not have a column with the time series values.
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ValueError: If `forecast` is provided but does not have a 'time' column.
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ValueError: If `forecast` is provided but does not have a column with the forecast values.
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ValueError: If `confidence_interval` is provided but `forecast` does not have 'lower' and 'upper' columns.
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"""
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if 'time' not in data.columns:
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raise ValueError("Data DataFrame must have a 'time' column.")
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value_column = next((col for col in data.columns if col != 'time'), None)
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if value_column is None:
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raise ValueError("Data DataFrame must have a column with the time series values.")
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plt.figure(figsize=figsize)
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plt.plot(data['time'], data[value_column], label="Historical Data", color=data_color)
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if forecast is not None:
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if 'time' not in forecast.columns:
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raise ValueError("Forecast DataFrame must have a 'time' column.")
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forecast_column = next((col for col in forecast.columns if col != 'time'), None)
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if forecast_column is None:
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raise ValueError("Forecast DataFrame must have a column with the forecast values.")
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plt.plot(forecast['time'], forecast[forecast_column], label="Forecast", color=forecast_color)
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if confidence_interval is not None:
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if 'lower' not in forecast.columns or 'upper' not in forecast.columns:
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raise ValueError("Forecast DataFrame must have 'lower' and 'upper' columns for confidence intervals.")
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plt.fill_between(forecast['time'], forecast['lower'], forecast['upper'], color=confidence_color, alpha=0.5, label="Confidence Interval")
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plt.xlabel(xlabel)
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plt.ylabel(ylabel)
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plt.title(title)
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plt.legend()
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plt.grid(True)
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plt.xticks(rotation=45)
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plt.tight_layout()
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def plot_residuals(
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residuals: pd.Series,
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title: str = "Residual Plot",
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xlabel: str = "Time",
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ylabel: str = "Residuals",
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figsize: Tuple[int, int] = (12, 6)
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) -> None:
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"""
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Plots the residuals of a time series forecast.
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Args:
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residuals (pd.Series): Pandas Series containing the residuals.
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title (str, optional): Title of the plot. Defaults to "Residual Plot".
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xlabel (str, optional): Label for the x-axis. Defaults to "Time".
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ylabel (str, optional): Label for the y-axis. Defaults to "Residuals".
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figsize (Tuple[int, int], optional): Figure size (width, height). Defaults to (12, 6).
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Raises:
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TypeError: If `residuals` is not a pandas Series.
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"""
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if not isinstance(residuals, pd.Series):
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raise TypeError("Residuals must be a pandas Series.")
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plt.figure(figsize=figsize)
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plt.plot(residuals.index, residuals.values, marker='o', linestyle='-', label="Residuals")
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plt.axhline(y=0, color='r', linestyle='--', label="Zero Line") # Add a horizontal line at y=0
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plt.xlabel(xlabel)
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plt.ylabel(ylabel)
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plt.title(title)
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plt.legend()
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plt.grid(True)
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plt.xticks(rotation=45)
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plt.tight_layout()
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def plot_acf(
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data: pd.Series,
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lags: int = 40,
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title: str = "Autocorrelation Function (ACF)",
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figsize: Tuple[int, int] = (12, 6)
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) -> None:
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"""
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Plots the Autocorrelation Function (ACF) of a time series.
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Args:
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data (pd.Series): Pandas Series containing the time series data.
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lags (int, optional): Number of lags to plot. Defaults to 40.
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title (str, optional): Title of the plot. Defaults to "Autocorrelation Function (ACF)".
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figsize (Tuple[int, int], optional): Figure size (width, height). Defaults to (12, 6).
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Raises:
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TypeError: If `data` is not a pandas Series.
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ValueError: If `lags` is not a positive integer.
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"""
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from statsmodels.graphics.tsaplots import plot_acf as sm_plot_acf
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if not isinstance(data, pd.Series):
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raise TypeError("Data must be a pandas Series.")
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if not isinstance(lags, int) or lags <= 0:
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raise ValueError("Lags must be a positive integer.")
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fig, ax = plt.subplots(figsize=figsize)
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sm_plot_acf(data, lags=lags, ax=ax, title=title)
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plt.tight_layout()
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def plot_pacf(
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data: pd.Series,
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lags: int = 40,
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title: str = "Partial Autocorrelation Function (PACF)",
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figsize: Tuple[int, int] = (12, 6)
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) -> None:
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"""
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Plots the Partial Autocorrelation Function (PACF) of a time series.
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Args:
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data (pd.Series): Pandas Series containing the time series data.
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lags (int, optional): Number of lags to plot. Defaults to 40.
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title (str, optional): Title of the plot. Defaults to "Partial Autocorrelation Function (PACF)".
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figsize (Tuple[int, int], optional): Figure size (width, height). Defaults to (12, 6).
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Raises:
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TypeError: If `data` is not a pandas Series.
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ValueError: If `lags` is not a positive integer.
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"""
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from statsmodels.graphics.tsaplots import plot_pacf as sm_plot_pacf
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if not isinstance(data, pd.Series):
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raise TypeError("Data must be a pandas Series.")
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if not isinstance(lags, int) or lags <= 0:
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raise ValueError("Lags must be a positive integer.")
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fig, ax = plt.subplots(figsize=figsize)
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sm_plot_pacf(data, lags=lags, ax=ax, title=title)
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plt.tight_layout()
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if __name__ == '__main__':
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# Example Usage
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try:
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# Create sample data
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dates = pd.date_range(start='2023-01-01', periods=100, freq='D')
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values = np.random.randn(100).cumsum()
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data = pd.DataFrame({'time': dates, 'value': values})
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# Create sample forecast
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forecast_dates = pd.date_range(start='2023-04-11', periods=30, freq='D')
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forecast_values = np.random.randn(30).cumsum() + values[-1]
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lower_bound = forecast_values - np.abs(np.random.randn(30).cumsum())
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upper_bound = forecast_values + np.abs(np.random.randn(30).cumsum())
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forecast = pd.DataFrame({'time': forecast_dates, 'forecast': forecast_values, 'lower': lower_bound, 'upper': upper_bound})
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# Plot time series and forecast
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plot_time_series(data, forecast, confidence_interval=(0.05, 0.95), title="Example Time Series Forecast")
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plt.show()
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# Create sample residuals
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residuals = pd.Series(np.random.randn(100), index=dates)
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# Plot residuals
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plot_residuals(residuals, title="Example Residual Plot")
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plt.show()
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# Plot ACF
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plot_acf(data['value'], lags=20, title="Example ACF Plot")
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plt.show()
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# Plot PACF
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plot_pacf(data['value'], lags=20, title="Example PACF Plot")
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plt.show()
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except ValueError as e:
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print(f"ValueError: {e}")
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except TypeError as e:
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print(f"TypeError: {e}")
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except Exception as e:
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print(f"An unexpected error occurred: {e}")
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