Tech Insights

time series forecasting

Last updated May 16, 2025, generated by Sumble

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.

Find organizations using time series forecasting on Sumble →

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.

mentioned alongside time series forecasting in 4% (164) of relevant job posts

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.

mentioned alongside time series forecasting in 2% (88) of relevant job posts

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.

mentioned alongside time series forecasting in 2% (132) of relevant job posts

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.

mentioned alongside time series forecasting in 2% (84) of relevant job posts

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.

mentioned alongside time series forecasting in 2% (81) of relevant job posts

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.

mentioned alongside time series forecasting in 2% (68) of relevant job posts

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.

mentioned alongside time series forecasting in 1% (245) of relevant job posts

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.

mentioned alongside time series forecasting in 1% (73) of relevant job posts

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.

mentioned alongside time series forecasting in 5% (101) of relevant job posts

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.

mentioned alongside time series forecasting in 1% (67) of relevant job posts

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.

mentioned alongside time series forecasting in 0% (267) of relevant job posts