Definition: Time-Series Forecasting
Time-series forecasting, a technique for predicting future values based on historical data, is essential for demand forecasting, financial analysis, and operational planning. By analyzing data that we stored in the past, we can make informed decisions that can guide our business strategy and help us understand future trends.
Time-series forecasting, a technique for predicting future values based on historical data, is essential for demand forecasting, financial analysis, and operational planning. By analyzing data that we stored in the past, we can make informed decisions that can guide our business strategy and help us understand future trends.
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ARIMA Models
AutoRegressive Integrated Moving Average, or ARIMA, is a forecasting method that combines both an autoregressive model and a moving average model. Autoregression uses observations from previous time steps to predict future values using a regression equation.
AutoRegressive Integrated Moving Average, or ARIMA, is a forecasting method that combines both an autoregressive model and a moving average model. Autoregression uses observations from previous time steps to predict future values using a regression equation.
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Top Notebooks for Data Science and Machine learning
1. Google Colab https://colab.research.google.com
Free cloud-based Jupyter notebook environment by Google.
Provides free access to GPUs & TPUs (with usage limits).
Supports Python and integrates well with TensorFlow and PyTorch.
2. Kaggle Notebooks https://www.kaggle.com/code
Free hosted Jupyter notebooks for machine learning and data science.
Comes with pre-installed libraries like TensorFlow, PyTorch, and Scikit-learn.
Provides access to free GPUs (limited usage).
3. Jupyter Notebook https://jupyter.org
Open-source interactive computing environment.
Can be installed locally or used via free online platforms like MyBinder.
Supports multiple programming languages through kernels.
4. Deepnote https://deepnote.com
Cloud-based Jupyter-compatible notebook with collaboration features.
Free plan includes limited resources but supports real-time teamwork.
Easy integration with SQL and cloud storage services.
5. Gradient Notebooks (Paperspace) https://gradient.paperspace.com
Free Jupyter notebooks with cloud GPUs.
Supports deep learning frameworks like TensorFlow and PyTorch.
Provides free GPU usage with restrictions.
6. Hex https://hex.tech
A collaborative data science platform with notebook-style coding.
Free tier available for personal projects with limited resources.
Designed for SQL, Python, and visualization tools.
7. Zeppelin Notebooks (Apache Zeppelin) https://zeppelin.apache.org
Open-source multi-purpose notebook for big data analytics.
Supports Apache Spark, Hive, and SQL.
Can be self-hosted for free.
1. Google Colab https://colab.research.google.com
Free cloud-based Jupyter notebook environment by Google.
Provides free access to GPUs & TPUs (with usage limits).
Supports Python and integrates well with TensorFlow and PyTorch.
2. Kaggle Notebooks https://www.kaggle.com/code
Free hosted Jupyter notebooks for machine learning and data science.
Comes with pre-installed libraries like TensorFlow, PyTorch, and Scikit-learn.
Provides access to free GPUs (limited usage).
3. Jupyter Notebook https://jupyter.org
Open-source interactive computing environment.
Can be installed locally or used via free online platforms like MyBinder.
Supports multiple programming languages through kernels.
4. Deepnote https://deepnote.com
Cloud-based Jupyter-compatible notebook with collaboration features.
Free plan includes limited resources but supports real-time teamwork.
Easy integration with SQL and cloud storage services.
5. Gradient Notebooks (Paperspace) https://gradient.paperspace.com
Free Jupyter notebooks with cloud GPUs.
Supports deep learning frameworks like TensorFlow and PyTorch.
Provides free GPU usage with restrictions.
6. Hex https://hex.tech
A collaborative data science platform with notebook-style coding.
Free tier available for personal projects with limited resources.
Designed for SQL, Python, and visualization tools.
7. Zeppelin Notebooks (Apache Zeppelin) https://zeppelin.apache.org
Open-source multi-purpose notebook for big data analytics.
Supports Apache Spark, Hive, and SQL.
Can be self-hosted for free.
Google
Google Colab
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This paper introduces Coupled Biased Random Walks (CBRW), a novel unsupervised outlier detection method for categorical data with varied frequency distributions and noisy features. Unlike existing techniques, CBRW models feature value couplings using biased random walks to identify outliers effectively. It provides outlier scores that can evaluate object outlierness or assist in feature weighting and selection. Experiments show that CBRW significantly outperforms current state-of-the-art methods and enhances performance on data with noise.
https://www.ijcai.org/Proceedings/16/Papers/272.pdf
https://www.ijcai.org/Proceedings/16/Papers/272.pdf