About this Event
Group Discounts:
Save 10% when registering 3 or more participants
Save 15% when registering 10 or more participants
About the course:
Duration: 1 Full Day (8 Hours)
Delivery Mode: Classroom (In-Person)
Language: English
Credits: 8 PDUs / Training Hours
Certification: Course Completion Certificate
Refreshments: Lunch, Snacks and beverages will be provided during the session
Course Overview:
This one-day introductory course gives you a practical foundation in time-series analysis, enabling you to understand, interpret, and forecast trends using sequential data. You will learn essential concepts such as patterns, seasonality, trend behaviour, and basic forecasting techniques used across analytics, finance, and business decision-making. Through hands-on exercises, you will gain clarity on how time-series data behaves and how to apply simple forecasting models to real datasets. By the end of the course, you will be confident in analysing time-based data and using it to support smarter decisions.
Learning Objectives:
By the end of this course, you will:
- Understand time-series basics and key components like trend and seasonality.
- Analyse and interpret sequential data with clarity.
- Use moving averages and smoothing techniques effectively.
- Apply simple forecasting methods suitable for beginners.
- Recognize model patterns and their business impact.
- Prepare data for time-series analysis with essential techniques.
- Use visualisations to detect patterns, anomalies, and shifts in data.
Target Audience:
Data analysts, statisticians, and finance professionals
Why is it the Right Fit for You?
This course is ideal if you want to understand how time-based data behaves and how to use it for forecasting and decision-making. It focuses on practical, beginner-friendly methods that can be immediately applied in real-world analytics and business contexts. You will gain clarity through step-by-step explanations and hands-on exercises designed to build confidence. Our approach ensures that you not only learn the techniques but also understand how and when to use them effectively. By the end, you will be able to interpret trends and make informed predictions using time-series concepts.
©2025 Mangates Tech Solutions Pvt Ltd. This content is protected by copyright law. Copy or Reproduction without permission is prohibited.
Our Royalty Referral Program
Know a team or professional who could benefit from our workshops? Refer them and earn attractive royalties for every successful registration.
For royalty-related queries, contact [email protected]
Looking to strengthen your team’s analytical capabilities?
We offer in-house versions of this course tailored to your industry, data structure, and analytical tools. Whether your team works with finance data, operational metrics, or forecasting processes, the training can be customized to ensure maximum impact and relevance. This makes it easier for teams to apply time-series methods effectively in day-to-day projects.
📧 Contact us today to schedule a customized in-house session:
Agenda
Module 1: Introduction to Time Series
Info:
• What time-series data is and where it is used
• Key terms and concepts
• Difference between cross-sectional and time-based datasets
• Activity
Module 2: Components of Time Series
Info: • Trend, seasonality, and cyclic behaviour
• Irregular and random variations
• How patterns affect forecasting outcomes
• Activity
Module 3: Time-Series Visualization & Interpretation
Info:
• Plotting line charts and trend visuals
• Identifying key behaviours through graphs
• Detecting anomalies and outliers
• Activity
Module 4: Moving Averages & Smoothing Techniques
Info:
• Simple and weighted moving averages
• Smoothing noisy data
• When to use smoothing vs. direct forecasts
• Activity
Module 5: Basic Forecasting Concepts
Info:
• Introduction to naive forecasting
• Understanding lag and lead values
• Short-term vs long-term forecasting principles
• Activity
Module 6: Time-Series Models Overview
Info:
• Basics of ARIMA concepts (at a beginner level)
• Using autocorrelation ideas
• Model assumptions and time dependence
• Activity
Module 7: Data Preparation for Time-Series Analysis
Info:
• Handling missing values
• Resampling and frequency adjustments
• Transformations for stability
• Activity
Module 8: Real-World Applications & Case Insight
Info:
• Using time-series in finance, retail, operations, and analytics
• Common mistakes to avoid
• Practical interpretation tips
• Case Study
Event Venue & Nearby Stays
regus NV, Henderson - S Water St, 303 South Water Street #ste 200, Henderson, United States
USD 542.07 to USD 705.91
