A Practical Approach to Timeseries Forecasting Using Python - Time Series Forecasting (TSF) Using LSTM

The video tutorial covers the process of forecasting using date analysis. It explains the use of the pandas T series library to handle calendars, including U.S. Federal holidays and business days. The tutorial guides through setting up custom business days, defining past and prediction days, and using the date range function. It then demonstrates how to perform predictions using a model, including setting the shape of the prediction output. Finally, it covers predicting future values and applying inverse transformations to the prediction data.

What is the purpose of performing date analysis in forecasting?

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What types of calendars are required for the date analysis?

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Which library is mentioned for handling calendars in the forecasting process?

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How do you define custom business days in the context of this forecasting method?

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What are the past and prediction days set to in the example provided?

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Explain the significance of the date range function in the forecasting process.

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What is the final step mentioned for transforming the prediction copies?

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