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We're here to help you find itDiscover the powerful techniques of Time Series Forecasting using Python in just 3 days with Koenig Solutions. This comprehensive 24-hour course will help you understand the differences between time series data and cross-sectional data, and equip you to transform datasets into time series for analysis. You’ll start coding in Python, conduct accurate time-series analysis, and interpret results effectively. Learn to normalize data, handle special series like White Noise and Random Walks, and manage autocorrelation and unexpected shocks. Practical modules will ensure you grasp model selection, stationarity tests, integration use, and volatility measurement. By the end, you'll be forecasting future trends based on past data patterns, ready to apply these skills in real-world scenarios.
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USD
View Fees Breakdown
| Course Fee | 1,150 |
|
Total Fees |
1,150 (USD) |
♱ Excluding VAT/GST
You can request classroom training in any city on any date by Requesting More Information
♱ Excluding VAT/GST
You can request classroom training in any city on any date by Requesting More Information
Prerequisites for Time Series Forecasting using Python
To successfully undertake the training in the Time Series Forecasting using Python course, the following minimum requirements are recommended:
These requirements ensure that learners can effectively engage with the course material and apply the techniques learned to real-world time series data sets.
Time Series Forecasting using Python is a 3-day course designed to equip professionals with the skills to analyze, model, and forecast time-series data using Python.
The Time Series Forecasting using Python course equips students with the ability to analyze, model, and forecast time series data using Python. It covers essential concepts such as data transformation, autocorrelation, stationarity, and volatility, providing practical skills for real-world applications.
