Offered By: IBMSkillsNetwork
Predict Bitcoin Close Prices using Foundational Models
Bitcoin price prediction is within reach! Build a Time Series Forecasting model using IBM’s Tiny Time Mixer (TTM) foundational model. In this hands-on project, you'll learn how to preprocess time series data, train the model, and evaluate predictions to gain deeper insights into market trends. Perfect for data scientists, financial analysts, and tech enthusiasts, this project enhances your ability to make accurate predictions and sharpen your forecasting skills using real-world Bitcoin price data.
Continue readingGuided Project
Data Science
96 EnrolledAt a Glance
Bitcoin price prediction is within reach! Build a Time Series Forecasting model using IBM’s Tiny Time Mixer (TTM) foundational model. In this hands-on project, you'll learn how to preprocess time series data, train the model, and evaluate predictions to gain deeper insights into market trends. Perfect for data scientists, financial analysts, and tech enthusiasts, this project enhances your ability to make accurate predictions and sharpen your forecasting skills using real-world Bitcoin price data.
Accurate time series forecasting is critical in industries such as finance, healthcare, and logistics. In this guided project, you will learn how to build a powerful time series forecasting model using IBM’s Tiny Time Mixer (TTM). You will use the Bitcoin Price dataset and preprocess it, fine-tune the model, and evaluate its performance on unseen data, providing insights that can help predict trends, optimize operations, or make data-driven decisions.
Foundational models such as Tiny Time Mixer (TTM) are particularly effective for time series forecasting because they are pre-trained on large datasets and designed to capture complex patterns in temporal data. Unlike traditional machine learning models, which often struggle with longer sequences and fail to generalize well, foundational models excel at learning and predicting from large amounts of data, making them ideal for tasks like forecasting financial trends, market movements, or any other sequential data.
- Data Preparation: You’ll start by loading a dataset and performing essential preprocessing steps.
- Model Setup: Learn how to load the Tiny Time Mixer (TTM) model, configure training arguments, and fine-tune the model on your dataset using advanced techniques like early stopping and learning rate scheduling.
- Evaluation: After training, you’ll evaluate the model using key performance metrics, such as Root Mean Squared Error (RMSE), and visualize the predictions to compare them with actual data.
- Making Predictions: With the trained model, you’ll predict future values based on historical trends and assess how well the model generalizes.
In just 1 hour, you will gain hands-on experience in building a time series forecasting pipeline, training the model, and analyzing its results. The insights gained from this project are transferable to real-world applications, such as predicting stock market trends, sales forecasts, or sensor data patterns.
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What you'll learn
- Generate and preprocess a time series dataset.
- Learn to split the dataset into training, validation, and test sets.
- Use the zero-shot learning to make predictions.
- Fine-tune the Tiny Time Mixer (TTM) model for time series forecasting.
- Evaluate the performance of the model using metrics such as RMSE.
- Visualize and compare predicted values with actual time series data.
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Significance of this project
- Hands-on experience: Build a practical time series forecasting model using IBM’s Tiny Time Mixer (TTM).
- End-to-end process: Learn the full pipeline from dataset creation and preprocessing to model training and evaluation.
- Enhance decision-making: Apply the model to real-world scenarios, improving predictive analytics for various industries.
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Who should enroll
- Data scientists and machine learning engineers who want to enhance their time series forecasting skills.
- Financial analysts who need to predict market trends based on historical data.
- Tech enthusiasts who are interested in learning AI techniques for time series data.
- Students or professionals looking to build forecasting models for sales, demand, or sensor data.
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What you'll need
- Basic knowledge of Python programming and machine learning.
- Familiarity with time series data and concepts is helpful but not mandatory.
- A reliable internet connection to access cloud-based tools and environments.
- A web browser (Chrome, Edge, Firefox, Safari).
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Why enroll
Estimated Effort
1 Hour
Level
Intermediate
Skills You Will Learn
Machine Learning, Python, Time Series
Language
English
Course Code
GPXX0V30EN