How Time Series Forecasting Can Help Business Make Better Decisions

Overview

Discover practical insights into time series forecasting for business decisions, including data quality importance, EDA benefits, model selection considerations, multi-series relationships, and monitoring challenges.

Syllabus

• Introduction to Time Series Forecasting
Overview and definition of time series
Importance of time series forecasting in business
• Data Quality and Preprocessing
Significance of data quality in forecasting
Techniques for cleaning and preprocessing time series data
Handling missing data and outliers
• Exploratory Data Analysis (EDA) for Time Series
Visualizing time series data
Identifying trends, seasonality, and cyclic patterns
Understanding autocorrelation and partial autocorrelation functions
• Model Selection in Time Series Forecasting
Overview of time series models: ARIMA, Exponential Smoothing, etc.
Criteria for selecting appropriate models
Model validation and performance metrics
• Multi-Series and Hierarchical Forecasting
Dealing with multiple time series simultaneously
Aggregated and disaggregated series forecasting
Relationships and dependencies across series
• Monitoring and Updating Forecasting Models
Importance of monitoring forecast performance
Techniques for model updating and recalibration
Managing model drift and data changes over time
• Challenges and Best Practices
Common challenges in time series forecasting
Developing robust forecasting processes
Case studies and real-world applications
• Conclusion and Future Trends
Summarizing key insights
Emerging trends and technologies in time series forecasting for business decisions

Subjects

Data Science