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Time-Series Forecasting

Master classical and modern time-series forecasting techniques using statistical models, machine learning, and deep learning to predict trends, season
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Time-series forecasting plays a critical role in decision-making across nearly every industry. From predicting sales and demand to forecasting weather, energy consumption, financial markets, healthcare trends, and system performance, time-series data provides insights that guide planning, optimisation, and risk management. As organisations become increasingly data-driven, the ability to model temporal patterns and generate reliable forecasts has become an essential skill for data scientists, analysts, engineers, and AI practitioners.
 
Unlike traditional machine learning problems, time-series forecasting involves ordered data, where time itself is a fundamental variable. Observations are not independent; instead, they are influenced by trends, seasonal cycles, recurring patterns, sudden shocks, and long-term dependencies. This makes forecasting both challenging and uniquely powerful. A strong forecasting system can help organisations anticipate future events, reduce uncertainty, optimise resources, and respond proactively to change.
 
The Time-Series Forecasting course by Uplatz provides a comprehensive, end-to-end exploration of forecasting techniques — from classical statistical models to modern machine learning and deep learning approaches. The course is designed to build strong conceptual foundations while also offering practical, hands-on experience using real-world datasets and industry-standard tools. Learners will understand not only how to build forecasting models, but also how to evaluate them, deploy them, and adapt them to evolving data.
🔍 What Is Time-Series Forecasting?
 
Time-series forecasting is the process of using historical time-ordered data to predict future values. The goal is to identify underlying patterns in the data and extrapolate them forward in time.
 
Key components of time-series data include:

  • Trend – long-term movement in the data

  • Seasonality – recurring patterns at fixed intervals (daily, weekly, yearly)

  • Cyclicality – long-term cycles without fixed frequency

  • Noise – random variation or unexplained fluctuations

Forecasting models aim to capture these components accurately while remaining robust to missing data, outliers, and structural changes.
 
This course covers forecasting at multiple levels:

  • Univariate forecasting (single time-series)

  • Multivariate forecasting (multiple influencing variables)

  • Short-term and long-term forecasting

  • Batch forecasting and real-time forecasting

⚙️ How Time-Series Forecasting Works
 
Time-series forecasting involves several structured steps, each of which is explored in depth in this course:
 
1. Data Understanding & Preparation

  • Time indexing and frequency alignment

  • Handling missing values

  • Outlier detection and treatment

  • Stationarity analysis

  • Data transformations (log, differencing, scaling)

2. Exploratory Time-Series Analysis

  • Visualisation of trends and seasonality

  • Autocorrelation (ACF) and partial autocorrelation (PACF)

  • Decomposition (additive and multiplicative)

3. Classical Statistical Models

  • Naïve and moving average models

  • AR, MA, ARMA

  • ARIMA and SARIMA

  • Exponential smoothing (SES, Holt, Holt-Winters)

4. Machine Learning-Based Forecasting

  • Feature engineering for time-series

  • Lag features and rolling statistics

  • Tree-based models (Random Forest, XGBoost)

  • Regression-based forecasting

5. Deep Learning for Time-Series

  • Recurrent Neural Networks (RNNs)

  • LSTM and GRU networks

  • Temporal Convolutional Networks (TCN)

  • Transformer-based forecasting models

6. Model Evaluation & Validation

  • Train–test splits for time-series

  • Cross-validation for temporal data

  • Forecast accuracy metrics (MAE, RMSE, MAPE, SMAPE)

🏭 Where Time-Series Forecasting Is Used in the Industry
 
Time-series forecasting is a foundational capability across industries:
 
1. Finance & Economics
 
Stock prices, volatility, risk metrics, interest rates, inflation forecasting.
 
2. Retail & E-commerce
 
Demand forecasting, inventory planning, sales prediction, pricing optimisation.
 
3. Energy & Utilities
 
Electricity load forecasting, renewable energy generation, grid optimisation.
 
4. Healthcare
 
Patient admission forecasting, disease spread modelling, resource planning.
 
5. Manufacturing & Supply Chain
 
Production planning, predictive maintenance, logistics optimisation.
 
6. Telecommunications & IT
 
Network traffic forecasting, capacity planning, anomaly detection.
 
7. Climate & Environmental Science
 
Weather forecasting, rainfall prediction, climate trend analysis.
 
Organisations rely on accurate forecasts to reduce costs, improve efficiency, and mitigate risk.
🌟 Benefits of Learning Time-Series Forecasting
 
By mastering time-series forecasting, learners gain:

  • Ability to model temporal patterns effectively

  • Strong foundation in both statistical and ML approaches

  • Skills to handle real-world forecasting challenges

  • Practical experience with Python forecasting libraries

  • Capability to build explainable and reliable prediction systems

  • High-demand skills applicable across many industries

Forecasting expertise is especially valuable in roles that require strategic planning and data-driven decision-making.
📘 What You’ll Learn in This Course
 
You will explore:

  • Core concepts of time-series data

  • Decomposition of trend, seasonality, and noise

  • ARIMA and SARIMA modelling

  • Exponential smoothing techniques

  • Feature engineering for ML-based forecasting

  • LSTM, GRU, and transformer-based forecasting

  • Forecast evaluation and error analysis

  • Multi-step and multivariate forecasting

  • Deploying forecasting models into production

  • Case studies using real-world datasets

🧠 How to Use This Course Effectively

  • Start with classical models to build intuition

  • Visualise data extensively before modelling

  • Compare statistical and ML approaches

  • Practice forecasting with different horizons

  • Evaluate models using appropriate metrics

  • Build an end-to-end forecasting pipeline

  • Complete the capstone project using real data

👩‍💻 Who Should Take This Course

  • Data Scientists

  • Data Analysts

  • Machine Learning Engineers

  • Business Analysts

  • Operations & Supply Chain Professionals

  • Economists & Financial Analysts

  • Students entering data science or AI fields

Basic Python knowledge is helpful but not mandatory.
🚀 Final Takeaway
 
Time-series forecasting transforms historical data into actionable insights about the future. By mastering both classical and modern forecasting techniques, learners gain the ability to anticipate trends, optimise operations, and support strategic decision-making across industries.

Course Objectives Back to Top

By the end of this course, learners will:

 

  • Understand time-series components and structures

  • Build classical forecasting models (ARIMA, SARIMA, ETS)

  • Apply machine learning to forecasting problems

  • Use deep learning for complex temporal patterns

  • Evaluate and compare forecasting models

  • Design scalable forecasting pipelines

  • Deploy forecasting models for real-world use cases

Course Syllabus Back to Top

Course Syllabus

Module 1: Introduction to Time-Series Data

  • What is time-series forecasting?

  • Use cases and challenges

Module 2: Data Preparation & Exploration

  • Handling missing values

  • Stationarity and transformations

Module 3: Time-Series Decomposition

  • Trend, seasonality, noise

Module 4: Classical Forecasting Models

  • Moving averages

  • AR, MA, ARIMA

Module 5: Seasonal Models

  • SARIMA

  • Holt-Winters

Module 6: Forecast Evaluation

  • Error metrics

  • Validation strategies

Module 7: Machine Learning Approaches

  • Feature engineering

  • Tree-based models

Module 8: Deep Learning for Forecasting

  • LSTM and GRU

  • Temporal CNNs

Module 9: Advanced Topics

  • Multivariate forecasting

  • Probabilistic forecasting

Module 10: Deployment & Production

  • Forecast pipelines

  • Monitoring and updates

Module 11: Capstone Project

  • End-to-end forecasting system

Certification Back to Top

Learners receive a Uplatz Certificate in Time-Series Forecasting, validating their ability to build, evaluate, and deploy forecasting models using statistical, machine learning, and deep learning techniques.

Career & Jobs Back to Top

This course prepares learners for roles such as:

  • Data Scientist

  • Forecasting Analyst

  • Machine Learning Engineer

  • Business Intelligence Analyst

  • Quantitative Analyst

  • Operations Research Analyst

Interview Questions Back to Top

1. What is time-series forecasting?

Predicting future values based on historical time-ordered data.

2. What is seasonality?

Recurring patterns at fixed time intervals.

3. What is stationarity?

A property where statistical characteristics remain constant over time.

4. What is ARIMA?

A statistical model combining autoregression, differencing, and moving averages.

5. What metrics evaluate forecasts?

MAE, RMSE, MAPE, SMAPE.

6. How does ML forecasting differ from classical methods?

ML uses engineered features instead of strict statistical assumptions.

7. Why is cross-validation difficult for time-series?

Because data order must be preserved.

8. What is multivariate forecasting?

Using multiple time-dependent variables to predict future values.

9. When are LSTMs useful?

For capturing long-term temporal dependencies.

10. Where are forecasts used in industry?

Finance, retail, energy, healthcare, supply chain, and climate science.

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BUY THIS COURSE (GBP 12 GBP 29)