1. Time forecasting

Data cleaning? Missing Values Duplicate Data Case Sensitivity Data Types Outliers

Quick Stats Basic Stats What Is an Outlier? How to Practice Basic Stats & Outliers

1. Variance 2. Standard Deviation (SD) 3. Coefficient of Variation (CV) 4 Practice Variance, Standard Deviation (SD), Coefficient of Variation (CV)

1. Skewness 2. Kurtosis

Correlation Why We Use Correlation? Pearson Correlation (r) Spearman Correlation (ρ or rₛ)

Trend Analysis 1. Time forecasting 2. Trend Break Detection 3 Moving Average

Grouping 1. Group By Sum 2. Group By Mean (Average) 3. Group By Count 4. Group By Minimum 5. Group By Maximum 6. Group By Median

AI Insights 1. Anomaly Detection 2 .Forecast Suggestion (Predictive Forecasting) 3 Correlation Warning 4 Trend Direction Prediction 5 Seasonality Detection 6 Top Driver / Influencer Analysis 7 Productivity Improvement Prediction 8 Business Risk Warnings

CERTIFICATION

chapter 7 : Trend Analysis

Time Forecasting — Explanation with Examples

Time forecasting is the technique of predicting future values based on past observations recorded over time. The data must follow a time order (daily, monthly, yearly), and this order is critical for prediction.

Explanation

In time forecasting:

We analyze historical time-based data
Identify patterns or direction
Extend those patterns to estimate future

Example 1: Sales Forecasting

Observation: Sales increase by ~20 each month

👉 Forecast for April = 260

Key Characteristics

Data is time-ordered
Past values influence future values
Used for planning and decision-making

Simple Business Meaning

Time forecasting helps businesses prepare for the future by estimating demand, revenue, or usage in advance using historical time patterns.

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Course Lessons

Course Lessons

chapter 7 : Trend Analysis