time series forecasting
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What is time series forecasting?
Time series forecasting is a statistical method used to predict future values based on historical time-ordered data. It's commonly used in finance to forecast stock prices, in retail to predict sales, in weather forecasting to predict future temperatures, and in various other fields where understanding trends and patterns over time is important.
What other technologies are related to time series forecasting?
time series forecasting Competitor Technologies
Regression
Regression models (e.g., Linear Regression) are alternative approaches to time series forecasting compared to specialized time series models like ARIMA or Exponential Smoothing. They can be used to predict future values based on past data but may require feature engineering.
LSTM
LSTM (Long Short-Term Memory) networks are a type of recurrent neural network (RNN) commonly used for time series forecasting. They are a competitor to traditional statistical methods, especially for complex, non-linear time series data.
neural networks
Neural networks, including various architectures like LSTMs and Transformers, offer an alternative to traditional statistical methods. They are suitable when handling intricate patterns and non-linear relationships.
SVM
Support Vector Machines (SVMs) can be adapted for time series forecasting, although less common than other methods. They can handle non-linear relationships but may require more preprocessing.
Random Forest
Random Forest is an ensemble learning method that can be used for time series forecasting by framing the problem as a regression task. While it's typically used for non-sequential data, it can still be applied, and may outperform classic methods.
Linear Regression
Linear Regression can be used for time series forecasting when the relationship between time and the target variable is approximately linear. It's a simpler alternative to more complex time series models.
deep learning
Deep learning models, especially recurrent neural networks (RNNs) and Transformers, provide advanced forecasting capabilities, especially for complex time series with non-linear dependencies. Therefore, they are a good alternative to simpler models.
XGBoost
XGBoost is a gradient boosting algorithm that can be used for time series forecasting, similarly to Random Forest. It can handle complex relationships but may require more hyperparameter tuning.
Number of organizations that mention technology
373
time series forecasting
1,429
Regression
1,196
LSTM
2,931
neural networks
1,368
SVM
1,677
Random Forest
662
Linear Regression
11,002
deep learning
2,669
XGBoost
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time series forecasting Complementary Technologies
anomaly detection
Anomaly detection techniques can be used to identify unusual patterns or outliers in time series data, which can improve the accuracy of forecasting models by removing or accounting for anomalous data points.
optimization
Optimization techniques are used to find the best parameters for time series models, e.g., minimizing the error function. They enhance the performance of any forecasting model.
scikit-learn
Scikit-learn is a Python library providing a wide range of machine learning tools, including some regression models usable for time series forecasting. It's helpful for implementing various forecasting approaches.
Number of organizations that mention technology
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Which job functions commonly mention time series forecasting?
Applied Scientist
0.2% of all Applied Scientist jobs mention time series forecasting
View 25
jobs on Sumble
Data Scientist
0.2% of all Data Scientist jobs mention time series forecasting
View 451
jobs on Sumble
AI Engineer
0.1% of all AI Engineer jobs mention time series forecasting
View 82
jobs on Sumble
Machine Learning
0.1% of all Machine Learning jobs mention time series forecasting
View 81
jobs on Sumble
Data, Analytics & Machine Learning
719 Data, Analytics & Machine Learning jobs mention time series forecasting
View 719
jobs on Sumble
Which organizations are mentioning time series forecasting?
Amazon
Retail Trade
Sanofi
Manufacturing
JPMorgan Chase
Finance and Insurance
Oracle
Scientific and Technical Services
Accenture
Professional Services
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