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Time Series Modelling Training

  • Training Duration:
  • 2 Days
  • Training Environment:
  • On-site at the Institution
  • In DVA Classrooms
  • Online
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About the Training

Time Series Modelling Training focuses on the analysis and forecasting of time series data. It is an in-depth training program designed specifically for data scientists, analysts, and professionals who use data-driven approaches to make strategic decisions in the business world. The training covers how to collect, process, analyze, and derive meaningful insights from time series data.

The program aims to teach participants the fundamentals and applications of time series models that can be applied to real-world scenarios, especially in business. The training includes key concepts such as time series analysis, forecasting methods, trend analysis, and seasonality. Participants will also learn advanced time series modeling techniques such as ARIMA (AutoRegressive Integrated Moving Average) and seasonal ARIMA.

Throughout the training, participants will use popular programming languages like Python to manipulate and analyze time series data. This includes practical skills such as cleaning, transforming, and visualizing time series data. The training also offers participants the opportunity to work with real-time data, allowing them to put theoretical knowledge into practice.

The Time Series Modelling Training program aims to teach participants the most up-to-date tools and techniques, while also informing them about the latest trends and applications in this field. By the end of the training, participants will be able to effectively analyze time series data, make accurate forecasts, and use this information to develop business strategies. These skills provide a competitive advantage in the business world and have the potential to make a significant impact on participants’ careers.

What Will You Learn?

This training program aims to provide participants with in-depth knowledge and skills in the following areas:
  • Fundamental concepts and characteristics of time series data
  • Visualization and analysis of time series data
  • Statistical properties of time series
  • Statistical modeling of time series
  • Forecasting and prediction analysis in time series
  • Use of common models such as ARIMA (AutoRegressive Integrated Moving Average) and GARCH (Generalized Autoregressive Conditional Heteroskedasticity)
  • Analysis and modeling of time series data using programming languages such as Python or R

Prerequisites

Who Should Attend?

The Time Series Modelling Training is suitable for the following professionals:
  • Data Analysts
  • Data Scientists
  • Financial Analysts
  • Marketing Analysts
  • Operations Managers
  • Business Intelligence Specialists
  • Economists
  • Professionals working with time series data in any sector
This training is beneficial for anyone who wants to understand, analyze, and forecast time series data.

Outline

Day 1: Introduction to Time Series and Basic Models Session 1: Overview of Time Series Analysis
  • Understanding Time Series Data
  • Importance and applications of Time Series Analysis
  • Components of Time Series: Trend, Seasonality, Cycle, Irregular component
Session 2: Basic Visualization and Analysis of Time Series Data
  • Plotting Time Series data
  • Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF)
  • Decomposition of Time Series data into its components
Session 3: Basic Models for Time Series Forecasting
  • Introduction to Moving Average and Exponential Smoothing Models
  • Understanding Autoregressive (AR) Models
  • Building a basic AR model in Python
Session 4: Hands-on Exercise
  • Participants will perform a basic analysis and modeling of a provided time seriesdataset
Day 2: Advanced Time Series Models and Forecasting Session 1: Advanced Models for Time Series Forecasting
  • Understanding ARIMA (Autoregressive Integrated Moving Average) and SARIMA(Seasonal ARIMA) models
  • Building an ARIMA model in Python
  • Choosing parameters for ARIMA/SARIMA models
Session 2: Model Validation and Forecasting
  • Testing the goodness of fit for a Time Series model
  • Out of sample validation techniques
  • Generating forecasts using Time Series models
Session 3: Introduction to Machine Learning for Time Series Forecasting
  • Basics of machine learning for Time Series
  • Implementing Recurrent Neural Networks (RNNs) and LSTM (Long Short TermMemory) networks for Time Series forecasting
Session 4: Hands-on Exercise and Course Wrap-up
  • Participants will build and validate an ARIMA model and generate forecasts, as wellas experiment with a basic machine learning model for Time Series forecasting
  • Review key concepts and methodologies, address outstanding questions
  • Discuss how these techniques can be applied to real-world problems, and potentialnext steps for further learning

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