exponential smoothing
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What is exponential smoothing?
Exponential smoothing is a time series forecasting method that uses weighted averages of past observations to predict future values. The weights decrease exponentially as observations come from further in the past; the smallest weights are associated with the oldest observations. This method is commonly used for forecasting sales, inventory, and other time-dependent data.
What other technologies are related to exponential smoothing?
exponential smoothing Competitor Technologies
ARIMA
ARIMA (Autoregressive Integrated Moving Average) is a time series forecasting method, similar to exponential smoothing. It accounts for autocorrelation in the data.
SVM
Support Vector Machines are a machine learning algorithm that can be used for time series forecasting as an alternative to exponential smoothing, especially when relationships are non-linear.
Regression
Regression techniques, particularly linear regression and its variants, can be used for time series forecasting, offering an alternative approach to exponential smoothing.
decision trees
Decision trees and ensemble methods based on them, like Random Forests, can be used for time series forecasting, providing a non-parametric alternative to exponential smoothing.
neural networks
Neural networks, especially recurrent neural networks (RNNs) like LSTMs, are powerful models for time series forecasting and can be used as a substitute for exponential smoothing, especially for complex and non-linear patterns.
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exponential smoothing Complementary Technologies
scikit-learn
Scikit-learn provides tools for model evaluation, selection, and preprocessing that can be used with exponential smoothing implementations. It can be used to compare exponential smoothing with other algorithms and enhance model building.
Pandas
Pandas provides data structures (like DataFrames) and data analysis tools that are useful for preparing time series data for exponential smoothing and analyzing the results.
TensorFlow
TensorFlow is a framework for building machine learning models, and while often used for more complex models like neural networks, it could potentially be used to implement or extend exponential smoothing variants. Can also be used to prepare data for exponential smoothing models.
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Which job functions commonly mention exponential smoothing?
Data Scientist
77 Data Scientist jobs mention exponential smoothing
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jobs on Sumble
Economist
3 Economist jobs mention exponential smoothing
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jobs on Sumble
AI Engineer
16 AI Engineer jobs mention exponential smoothing
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jobs on Sumble
Machine Learning
19 Machine Learning jobs mention exponential smoothing
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jobs on Sumble
Statistician
4 Statistician jobs mention exponential smoothing
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jobs on Sumble
Which organizations are mentioning exponential smoothing?
Qualcomm
Scientific and Technical Services
Accenture
Professional Services
UST Global
Professional Services
EXL
Professional Services
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