Zettelkasten/Terminology Information95 MIC (Maximum Information Coefficient) Information MIC (Maximum Information Coefficient) is a measure of the strength of the linear or nonlinear association between two variables. MIC ranges between 0 and 1, where 0 indicates no association and 1 indicates perfect association. It is a nonparametric method that uses mutual information as a measure of association between two variables. MIC is scale-invariant, which means it can be used.. Zettelkasten/Terminology Information 2023. 4. 20. ReLU (Rectified Linear Unit) Information ReLU (Rectified Linear Unit) is a type of activation function used in neural networks, which is defined as f(x) = max(0,x). It returns 0 for all negative inputs and returns the input as it is for all positive inputs. ReLU is a popular choice for activation function due to its simplicity and effectiveness in reducing vanishing gradients in deep neural networks. Vanishing gradients occ.. Zettelkasten/Terminology Information 2023. 4. 19. MLP (Multi-Layer Perceptron) Information MLP (Multi-Layer Perceptron) is a type of feedforward artificial neural network (ANN) that consists of multiple layers of nodes. It is composed of an input layer, one or more hidden layers, and an output layer. Each node in a layer is connected to every node in the next layer, forming a dense graph. MLP is trained using backpropagation, a supervised learning technique that adjusts th.. Zettelkasten/Terminology Information 2023. 4. 18. RES (Renewable Energy Source) Information RES (Renewable Energy Source) RES (Renewable Energy Source) is source of energy that are replenished naturally and can be replenished over a relatively short period of time. Examples of RES (Renewable Energy Source) include solar, wind, hydro, geothermal, and biomass. RES (Renewable Energy Source) are a growing area of interest due to concerns about climate change and the need to red.. Zettelkasten/Terminology Information 2023. 4. 17. ESS (Energy Storage System) Information An ESS (Energy Storage System) is a technology that stores energy in the form of electrical energy, chemical energy, or thermal energy, for later use. ESS can help to balance the energy supply and demand in the power grid, and integrate renewable energy sources by storing excess energy and releasing it when needed. ESS can also help to provide backup power during outages, reduce peak.. Zettelkasten/Terminology Information 2023. 4. 16. BC Hydro (British Columbia Hydro) dataset Information BC Hydro (British Columbia Hydro) is a Canadian electric utility company, serving customers in the province of British Columbia. The BC Hydro dataset provides hourly electricity demand data for the province of British Columbia. The dataset contains historical hourly electricity demand data for the entire province, as well as for specific regions and communities within the province. T.. Zettelkasten/Terminology Information 2023. 4. 16. EMS (Energy Management System) Information EMS (Energy Management System) is a computer-based system that helps to monitor, control and optimize energy usage and consumption in a building, factory or organization. It helps to improve the efficiency of energy usage by monitoring and controlling the energy consumption of various equipment and systems in real-time. EMS systems use various data collection techniques, including su.. Zettelkasten/Terminology Information 2023. 4. 15. Univariate forecasting Information Univariate forecasting is a time series forecasting technique that involves analyzing a single variable's past values to predict its future values. It assumes that the variable of interest's past values are the most relevant predictor of its future values. Univariate models do not consider the relationship between the variable of interest and other related variables. The most common .. Zettelkasten/Terminology Information 2023. 4. 14. Multivariate forecasting Information Multivariate forecasting is a type of time series forecasting that involves predicting multiple variables simultaneously. This approach uses the relationships and dependencies between the variables to make accurate predictions. Multivariate forecasting can be useful in situations where the variables are interdependent and affect each other, making it difficult to predict one variable.. Zettelkasten/Terminology Information 2023. 4. 13. Fourier analysis Information Fourier analysis is a mathematical technique used to represent a time series as a sum of sine and cosine functions with different frequencies. This technique allows us to decompose a complex signal into simpler components that can be easily analyzed and modeled. In time series forecasting, Fourier analysis is used to identify seasonal patterns or cycles in the data. The output of Fou.. Zettelkasten/Terminology Information 2023. 4. 12. STL (Seasonal-Trend decomposition using LOESS) Information The STL (Seasonal-Trend decomposition using LOESS) is a method used to decompose time series data into three main components: trend, seasonal, and residual. The trend component represents the long-term pattern or behavior in the time series. The seasonal component represents the repeating pattern that occurs within the time series over a fixed period of time, such as weekly or monthl.. Zettelkasten/Terminology Information 2023. 4. 11. Time series forecasting Information Time series forecasting is a technique used to make predictions about future values based on past observations of a time series dataset. A time series is a sequence of data points recorded over time, typically at regular intervals. The goal of time series forecasting is to identify patterns and trends in the time series data and use them to predict future values. Time series forecast.. Zettelkasten/Terminology Information 2023. 4. 10. Transformer Information Transformer is a deep learning architecture that was introduced in a 2017 paper by Vaswani et al. for natural language processing tasks. Unlike traditional RNNs, Transformers don't use sequential processing to learn context from a sequence of inputs. Instead, they use a self-attention mechanism to process all input positions simultaneously. The architecture of the Transformer is comp.. Zettelkasten/Terminology Information 2023. 4. 9. RNN (Recurrent Neural Network) Information RNN (Recurrent Neural Network) is a type of neural network designed for processing sequential data, such as time series or natural language. It can handle input of varying length, and maintain an internal state (memory) that allows it to capture information from previous inputs. RNNs can be trained with backpropagation through time (BPTT) to optimize their weights and learn to predic.. Zettelkasten/Terminology Information 2023. 4. 8. Long-term time series forecasting Information Long-term time series forecasting refers to the prediction of a time series for a horizon that is typically greater than a year. This type of forecasting is often used in business and economics, such as for predicting stock prices, interest rates, and sales data for a company. Long-term time series forecasting can be challenging due to the high number of variables that can affect the.. Zettelkasten/Terminology Information 2023. 4. 7. RW (Random Walk) Information The RW (Random Walk) algorithm is a time series forecasting technique that assumes that the future values of a time series are equal to the last observed value plus a random error term. The RW algorithm is based on the principle that a series of random shocks in a time series tends to maintain the direction of the series in the short term. The RW algorithm is widely used as a benchma.. Zettelkasten/Terminology Information 2023. 4. 6. SVR (Support Vector Regression) Information SVR is a type of supervised machine learning algorithm that can be used for regression tasks. It is based on Support Vector Machines (SVM) and uses a similar approach to find a function that best fits the data. The goal of SVR is to find the hyperplane that has the maximum margin of error within a certain threshold. The threshold is defined by the epsilon parameter, which determines .. Zettelkasten/Terminology Information 2023. 4. 5. BPN (BackPropagation Neural Network) Information BPN (BackPropagation Neural Network) is a type of artificial neural network where the input signal is fed forward through the network, producing an output signal after passing through multiple layers of processing units, also called neurons. BPN employs backpropagation algorithm to adjust the weights of connections between neurons based on the difference between the expected output a.. Zettelkasten/Terminology Information 2023. 4. 4. MASE (Mean Absolute Scaled Error) Information MASE (Mean Absolute Scaled Error) is a metric for measuring forecast accuracy. MASE compares the accuracy of a given forecast with that of a naive forecast (e.g., a seasonal naïve or a random walk model). MASE is scale-independent, making it useful for comparing the accuracy of forecasts across different time series with different scales. The formula for MASE is: MASE = mean(|e_t|) /.. Zettelkasten/Terminology Information 2023. 4. 3. Classification Information Classification is a task in machine learning that involves assigning a label or class to input data based on its features or characteristics. It is a supervised learning approach that involves training a model on a labeled dataset and using it to predict the class of new, unseen data. There are many types of classification models, including decision trees, logistic regression, naive .. Zettelkasten/Terminology Information 2023. 4. 2. 이전 1 2 3 4 5 다음