Time Series Analytics and Forecasting
Unleash Insights from Time Series Data
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About
Are you ready to unravel the mysteries hidden within time series data? Look no further than the Time Series Guide! Repository is packed with resources, project ideas, and tips to help you master the art of time series analysis
Have you ever wondered what the stock market will look like next month? Or how the weather will be next week? Or what will be the sales of the store in next quarter
Major difference between time series analysis and interpolation methods is that time series analysis is focused more on analyzing data over time to identify patterns, and trends. While interpolation methods are focused on estimating missing data points between known data points.
Time series analysis ----> When predicting future values is important
Interpolation methods ----> Filling in missing data.
Features
Project Ideas:
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Sales forecasting
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Stock market forecasting
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Weather forecasting
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Disease outbreak forecasting
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Traffic forecasting
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Energy demand forecasting
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Website traffic forecasting
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Supply chain forecasting
Un-Explored and New Ideas:
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Predicting social trends, for an example fashion and food which will be helpful for industry to understand and adapt according to it.
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Wildlife population forecasting, to predict wildlife populations based on historical data which is helpful to take the action at correct time.
Resources:
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Forecasting: Principles and Practice (Textbook)
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Time Series Cheatsheet (Cheatsheet)
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Machine learning for trading (Udacity-Course)
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Time Series with Python (Datacamp-course)
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11 Classical Time Series Forecasting Methods in Python (Cheatsheet)
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Forecasting Future Prices of Cryptocurrency using Historical Data (Blog)
For more course reasources, I have created a separate thread:
https://www.kaggle.com/discussions/general/310100#1706540
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