A Coding Implementation for Building Python-based Data and Business intelligence BI Web Applications with Taipy: Dynamic Interactive Time Series Analysis, Real-Time Simulation, Seasonal Decomposition, and Advanced Visualization

!pip install taipy statsmodels

from taipy.gui import Gui
import numpy as np
import matplotlib.pyplot as plt
from statsmodels.tsa.seasonal import seasonal_decompose

state = {
    "trend_coeff": 0.05,
    "seasonal_amplitude": 5.0,
    "noise_level": 1.0,
    "time_horizon": 100,
    "sim_data": None,
    "times": None,
    "plot_timeseries": None,
    "plot_decomposition": None,
    "summary": ""
}

def update_simulation(state):
    t = np.arange(state["time_horizon"])

    trend = state["trend_coeff"] * t

    season = state["seasonal_amplitude"] * np.sin(2 * np.pi * t / 7)

    noise = np.random.normal(0, state["noise_level"], size=state["time_horizon"])

    sim_data = trend + season + noise


    state["times"] = t
    state["sim_data"] = sim_data

    mean_val = np.mean(sim_data)
    std_val = np.std(sim_data)
    state["summary"] = f"Mean: {mean_val:.2f} | Std Dev: {std_val:.2f}"

    fig, ax = plt.subplots(figsize=(8, 4))
    ax.plot(t, sim_data, label="Simulated Data", color="blue")
    ax.set_title("Simulated Time Series")
    ax.set_xlabel("Time (days)")
    ax.set_ylabel("Value")
    ax.legend()
    state["plot_timeseries"] = fig

    try:
        decomp = seasonal_decompose(sim_data, period=7)

        fig_decomp, axs = plt.subplots(4, 1, figsize=(8, 8))
        axs[0].plot(t, decomp.observed, label="Observed", color="blue")
        axs[0].set_title("Observed Component")

        axs[1].plot(t, decomp.trend, label="Trend", color="orange")
        axs[1].set_title("Trend Component")

        axs[2].plot(t, decomp.seasonal, label="Seasonal", color="green")
        axs[2].set_title("Seasonal Component")

        axs[3].plot(t, decomp.resid, label="Residual", color="red")
        axs[3].set_title("Residual Component")

        for ax in axs:
            ax.legend()

        fig_decomp.tight_layout()
        state["plot_decomposition"] = fig_decomp
    except Exception as e:
        fig_err = plt.figure(figsize=(8, 4))
        plt.text(0.5, 0.5, f"Decomposition Error: {str(e)}",
                 horizontalalignment='center', verticalalignment='center')
        state["plot_decomposition"] = fig_err

update_simulation(state)


page = """
# Advanced Time Series Simulation and Analysis Dashboard


This dashboard simulates time series data using customizable parameters and performs a seasonal decomposition to extract trend, seasonal, and residual components.


## Simulation Parameters


Adjust the sliders below to modify the simulation:


- **Trend Coefficient:** Controls the strength of the linear trend.
- **Seasonal Amplitude:** Adjusts the intensity of the weekly seasonal component.
- **Noise Level:** Sets the randomness in the simulation.
- **Time Horizon (days):** Defines the number of data points (days) in the simulation.


<|{trend_coeff}|slider|min=-1|max=1|step=0.01|label=Trend Coefficient|on_change=update_simulation|>
<|{seasonal_amplitude}|slider|min=0|max=10|step=0.1|label=Seasonal Amplitude|on_change=update_simulation|>
<|{noise_level}|slider|min=0|max=5|step=0.1|label=Noise Level|on_change=update_simulation|>
<|{time_horizon}|slider|min=50|max=500|step=10|label=Time Horizon (days)|on_change=update_simulation|>


## Simulated Time Series


This plot displays the simulated time series based on your parameter settings:


<|pyplot|figure=plot_timeseries|>


## Seasonal Decomposition


The decomposition below splits the time series into its observed, trend, seasonal, and residual components:


<|pyplot|figure=plot_decomposition|>


## Summary Statistics


{summary}


---


*This advanced dashboard is powered by Taipy's reactive engine, ensuring real-time updates and an in-depth analysis experience as you adjust the simulation parameters.*
"""


Gui(page).run(state=state, notebook=True)