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37 lines
2.6 KiB
37 lines
2.6 KiB
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# Introduction to time series forecasting
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What is time series forecasting? It's the process of predicting future events by analyzing past trends.
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## Regional topic: worldwide electricity usage ✨
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In these two lessons, you will explore time series forecasting, a relatively lesser-known area of machine learning that is highly valuable for applications in industry, business, and other fields. While neural networks can enhance the effectiveness of these models, we will focus on classical machine learning approaches to predict future outcomes based on historical data.
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Our regional focus is on global electricity usage, an intriguing dataset that helps us learn how to forecast future power consumption by analyzing past load patterns. This type of forecasting can be incredibly useful in a business context.
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Photo by [Peddi Sai hrithik](https://unsplash.com/@shutter_log?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) of electrical towers on a road in Rajasthan on [Unsplash](https://unsplash.com/s/photos/electric-india?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText)
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## Lessons
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1. [Introduction to time series forecasting](1-Introduction/README.md)
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2. [Building ARIMA time series models](2-ARIMA/README.md)
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3. [Building Support Vector Regressor for time series forecasting](3-SVR/README.md)
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## Credits
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"Introduction to time series forecasting" was created with ⚡️ by [Francesca Lazzeri](https://twitter.com/frlazzeri) and [Jen Looper](https://twitter.com/jenlooper). The notebooks were originally published in the [Azure "Deep Learning For Time Series" repo](https://github.com/Azure/DeepLearningForTimeSeriesForecasting) authored by Francesca Lazzeri. The SVR lesson was written by [Anirban Mukherjee](https://github.com/AnirbanMukherjeeXD)
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---
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**Disclaimer**:
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This document has been translated using the AI translation service [Co-op Translator](https://github.com/Azure/co-op-translator). While we strive for accuracy, please note that automated translations may contain errors or inaccuracies. The original document in its native language should be regarded as the authoritative source. For critical information, professional human translation is recommended. We are not responsible for any misunderstandings or misinterpretations resulting from the use of this translation. |