Day 25 of 30-Day Challenge: Learning Gen AI and LLM's
Rupali Raosaheb Darade
Senior project and test Manager & Quantum Ambassador at IBM, Product Manager, Product Owner, GenAI Leader, Strategic Thinker, Project and Test Management for SAP/Non-SAP Projects, Enterprise Design Thinking Co-Creator
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Generative AI for Time Series Forecasting
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The Magical Crystal Ball
?Imagine you have a magical crystal ball that can predict what will happen in the future. You can ask it questions like "Will it rain tomorrow?" or "How many ice creams will I sell at my lemonade stand next week?"
?Instead of just making guesses, the crystal ball uses special powers to look at what happened in the past and make predictions about the future. This is kind of like what happens with time series forecasting.
?Time series forecasting is like trying to predict what will happen next in a sequence of events. For example, if you have a lemonade stand, you might want to predict how many cups of lemonade you will sell each day based on how many you sold in the past.
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The Magic of Generative AI
?Generative AI is like a superpower that helps us make predictions about the future. It's like having a team of experts who can look at all the data from the past and make predictions about what will happen next.
?One way that Generative AI can help with time series forecasting is by using something called sequence-to-sequence models. These models are like special machines that can take in a sequence of data, like the number of cups of lemonade sold each day, and output a new sequence of data that predicts what will happen next.
?Another way that Generative AI can help is by using graph neural networks. These networks are like special maps that show how all the different pieces of data are connected. They can help us understand how different factors, like the weather or the time of year, affect the number of cups of lemonade sold.
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Implementing the Magic
?So, how do we make this magic happen? We can use popular deep learning frameworks like TensorFlow or PyTorch to build our sequence-to-sequence models and graph neural networks.
?These frameworks provide special tools and libraries that make it easy to build and train our models. We can use them to input our data, train our models, and make predictions about the future.
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Techniques for Time Series Forecasting
Some other techniques that we can use for time series forecasting with Generative AI include:
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ARIMA: This is a special model that can help us understand how the data changes over time.
LSTM: This is a type of neural network that is especially good at handling sequential data.
Graph Attention Networks: These networks are like special maps that show how all the different pieces of data are connected.
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Generative AI is like a superpower that can help us make predictions about the future. By using sequence-to-sequence models and graph neural networks, we can build powerful models that can help us forecast what will happen next. With popular deep learning frameworks like TensorFlow and PyTorch, we can make this magic happen and start predicting the future today!
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?What topic would you like to explore next?
?Let ME know in the comments if there's a specific topic you'd like to explore next. I'll do my best to cover it in our upcoming posts.
Stay tuned for Day 26!
?I'll be back tomorrow with another exciting topic. Stay tuned and keep learning!