Essays about: "Performance forecast"
Showing result 1 - 5 of 237 essays containing the words Performance forecast.
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1. Visualization and analysis of object states using diffusion models and PyTorch
University essay from Mälardalens universitet/Akademin för innovation, design och teknikAbstract : Artificial Intelligence (AI) is an extremely rapidly growing field in modern technology. As the applications of AI expand, the ability to accurately analyze and predict the condition of various objects through various models has profound implications across numerous industries. READ MORE
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2. Volatility Forecasting - A comparative study of different forecasting models.
University essay fromAbstract : This study evaluates the out-of-sample forecasting performance of different volatility mod- els. When applied to XACT OMXS30, we use GARCH(1,1), EGARCH(1,1), and t- GAS(1,1) to forecast squared daily returns while Realized GARCH(1,1) and HAR-RV are used to forecast Realized Variance. READ MORE
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3. Forecasting Volatility of Ether- An empirical evaluation of volatility models and their capacity to forecast one-day-ahead volatility of Ether
University essay from Göteborgs universitet/Graduate SchoolAbstract : This study evaluates the performance of volatility models in forecasting one-day-ahead volatility of the cryptocurrency Ether. The selected models are: GARCH, EGARCH, GJR-GARCH, SMA9, SMA20, and EWMA. We investigate both in-sample performance and out-of-sample performance. READ MORE
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4. Sales forecasting for supply chain using Artificial Intelligence
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Supply chain management and logistics are two sectors currently experiencing a transformation thanks to the advent of AI(Artificial Intelligence) technologies. Leveraging predictive analytics powered by AI presents businesses with novel opportunities to streamline their operations effectively. READ MORE
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5. Restaurant Daily Revenue Prediction : Utilizing Synthetic Time Series Data for Improved Model Performance
University essay from Uppsala universitet/Avdelningen för beräkningsvetenskapAbstract : This study aims to enhance the accuracy of a demand forecasting model, XGBoost, by incorporating synthetic multivariate restaurant time series data during the training process. The research addresses the limited availability of training data by generating synthetic data using TimeGAN, a generative adversarial deep neural network tailored for time series data. READ MORE