In today’s data-driven world, understanding the past is crucial, but predicting the future is transformative. This interactive seminar equips data scientists, developers, and other curious minds with the practical skills to leverage the power of time-series forecasting.

Time-series data, a sequence of data points indexed by time, holds the key to unlocking future trends, anticipating market fluctuations, and making data-driven decisions with confidence.

This workshop goes beyond theory, offering:

• Two hands-on labs using real-world datasets, allowing you to roll up your sleeves and apply learned concepts to solve real-world forecasting problems.
• Practical implementations of popular forecasting methods, equipping you with tangible skills to use in your everyday work.
• Expert guidance throughout the sessions to ensure your success.

By the end of this seminar, you’ll be able to:

• Build and evaluate forecasting models using various techniques.
• Apply your skills to real-world problems across diverse industries.
• Make informed decisions based on data-driven insights into future trends.

This seminar is your opportunity to gain a competitive edge and unlock the power of time-series forecasting to take your data analysis and decision-making to the next level.

This seminar is tailored for developers, data scientists, and individuals with Python background who want to:

• Learn about time-series analysis and forecasting.
• Learn practical implementations of popular forecasting methods.
• Gain hands-on experience with real-world datasets and tools.
• Apply forecasting principles to solve specific business problems.

1. Basic understanding of data analysis: This includes knowledge of concepts like data exploration, data cleaning, and visualization.
2. Fundamental understanding of statistics: Familiarity with concepts like mean, median, standard deviation is essential.
3. Introduction to Python programming: Basic proficiency in Python is necessary, including using libraries like Pandas for data manipulation and NumPy for numerical computations.

Introduction to Time-Series Forecasting:

What is time-series data and its importance?

Applications of time-series forecasting across various industries.

Understanding different types of forecasting methods (e.g., statistical, machine learning, deep learning).

Time-Series Fundamentals:

Data exploration and cleaning techniques for time-series data using Python libraries (Pandas, numpy).

Useful plots and graph using Python libraries (matplotlib, seaborn, pandas)

Seasonality analysis and decomposition methods

Stationarity and its importance in forecasting

Statistical Forecasting Models:

Introduction to ARIMA (Autoregressive Integrated Moving Average) model and its components (AR, I, MA).

Building and evaluating ARIMA models using Python libraries (e.g., statsmodels).

Hands-on Lab Session – Applying ARIMA to predict Monthly Sales of French Champagne

Practical implementation of learned concepts with real-world datasets.

Guidance and support from instructors during the lab session

Machine Learning for Time-Series Forecasting:

Introduction to supervised learning algorithms for forecasting

Feature engineering techniques for time-series data.

Deep Learning for Time-Series Forecasting:

Introduction to recurrent neural networks (RNNs) like LSTMs for time-series forecasting.

Understanding the architecture and training process of LSTMs using Python libraries (TensorFlow).

Visualization of forecasting results and interpretation.

Hands-on Lab Session – Predicting stock prices using LSTM:

Practical implementation of learned concepts with real-world datasets.

Guidance and support from instructors during the lab session