Overview

Analysis Overview

Time Series Forecasting Configuration

Analysis overview and configuration

Configuration

Analysis TypeTime Series Forecasting
CompanyRetailCo
ObjectiveForecast daily retail store item demand using time series analysis with trend decomposition, ARIMA, ETS, and Prophet models
Analysis Date2026-02-24
Processing Idtest_1771923400
Total Observations4826

Module Parameters

ParameterValue_row
confidence_level0.95confidence_level
forecast_horizon30forecast_horizon
seasonal_period7seasonal_period
Time Series Forecasting analysis for RetailCo
Data Preparation

Data Preprocessing

Data Quality & Train/Test Split

Data preprocessing and column mapping

Data Quality

Initial Rows4826
Final Rows4826
Rows Removed0
Retention Rate100

Data Quality

MetricValue
Initial Rows4,826
Final Rows4,826
Rows Removed0
Retention Rate100%
Processed 4,826 observations, retained 4,826 (100.0%) after cleaning
Executive Summary

Executive Summary

Key Findings & Recommendations

Key Metrics

total_observations
4826
best_model
ETS
best_mape
30.84
trend_direction
increasing
seasonal_strength
0.3
forecast_horizon
30

Key Findings

MetricValue
Data Period4,826 daily observations
Best ModelETS
Forecast Accuracy (MAPE)30.8%
Trend DirectionIncreasing
Seasonal Strength0.3 (Moderate)
Forecast Horizon30 days ahead

Summary

Bottom Line: Time series forecasting analysis completed using 4,826 observations (13.2 days of daily sales data). The ETS model achieved 30.8% MAPE (Mean Absolute Percentage Error) on test data, indicating acceptable forecast accuracy.

Key Findings:
• Trend: Sales show a increasing trend pattern
• Seasonality: Moderate seasonal strength (0.30) - seasonal effects present but mixed with trend
• Best Model: ETS outperformed alternatives with 30.8% error
• Forecast Horizon: 30-day ahead predictions with 95% confidence intervals

Recommendation: Use the ets forecast for operational planning. Apply the 95% upper confidence bound for safety stock calculations to minimize stockout risk. Re-train the model weekly with recent data to maintain forecast accuracy as patterns evolve.
Figure 4

Historical Trends

Sales with Moving Averages

Historical sales with moving averages showing trend and volatility

Figure 5

STL Decomposition

Trend, Seasonal, and Residual Components

STL decomposition showing trend, seasonal, and residual components

Figure 6

Weekly Seasonality

Day-of-Week Sales Distribution

Day-of-week sales distribution showing weekly patterns

Figure 7

ACF/PACF Diagnostics

Autocorrelation Analysis

Autocorrelation and partial autocorrelation plots for model diagnostics

Figure 8

Forecast Results

Model Predictions with Confidence Intervals

Best model (ETS) forecast with 95% confidence intervals

Figure 9

Model Comparison

Accuracy Metrics Across Models

Performance comparison across all forecasting models tested

Figure 10

Residual Diagnostics

Model Error Distribution & Normality

Residual distribution analysis with normality assessment

Figure 11

Q-Q Plot

Residual Normality Check

Residual distribution analysis with normality assessment

Figure 12

Actual vs Predicted

Forecast Accuracy Scatter Plot

Actual vs predicted scatter plot showing forecast accuracy on test data

Figure 13

Store Comparison

Demand by Store Location

Demand comparison across stores showing volume leaders and laggards

Figure 14

Item Demand Ranking

Top Items by Sales Volume

Item demand ranking showing top sellers and slow movers

Figure 15

Demand Heatmap

Store x Item Demand Matrix

Store x item demand matrix showing cross-sectional patterns

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