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Timeseries Prediction in ML
- Python Automation and Machine Learning for ICs - - An Online Book: Python Automation and Machine Learning for ICs by Yougui Liao - |
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Python Automation and Machine Learning for ICs http://www.globalsino.com/ICs/ | ||||||||
Chapter/Index: Introduction | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | Appendix | ||||||||
================================================================================= Timeseries prediction in machine learning involves using historical data points, indexed in time order, to model and forecast future values. This process is crucial in various fields such as finance, weather forecasting, energy consumption, and stock market trends. The key characteristic of time series data is that successive values are dependent on previous values, often exhibiting seasonality, trends, and cyclic behaviors. In machine learning, time series prediction can be performed by using several methods:
Time series prediction typically involves preprocessing steps such as detrending, removing seasonality, and normalization to make the data more suitable for modeling. The choice of model and approach largely depends on the nature of the data, the specific characteristics of the time series, and the prediction requirements. The script below apply a linear regression model to predict future values:
The output of the script is below: =========================================== Timeseries prediction model (script) predicts semiconductor sales using historical data by using the Prophet library by Facebook, which is powerful for time series forecasting and handles trends and seasonality effectively:
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