Sökning: "evaluation metrics in time series"

Visar resultat 1 - 5 av 37 uppsatser innehållade orden evaluation metrics in time series.

1. 1. Neural Network-based Anomaly Detection Models and Interpretability Methods for Multivariate Time Series Data

   Master-uppsats, Stockholms universitet/Institutionen för data- och systemvetenskap

   Författare :Deepthy Prasad; Swathi Hampapura Sripada; [2023]
   Nyckelord :multivariate - time series; anomaly detection; neural networks; autoencoders; interpretability; counterfactuals;

   Sammanfattning : Anomaly detection plays a crucial role in various domains, such as transportation, cybersecurity, and industrial monitoring, where the timely identification of unusual patterns or outliers is of utmost importance. Traditional statistical techniques have limitations in handling complex and highdimensional data, which motivates the use of deep learning approaches. LÄS MER

3. 2. Dataset characteristics effect on time series forecasting : comparison of statistical and deep learning models

   Kandidat-uppsats, Högskolan i Halmstad/Akademin för informationsteknologi

   Författare :Adam Ahlman; Adam Taylor; [2023]
   Nyckelord :Time Series; Forecasting;

   Sammanfattning : Time series are points of data measured throughout time in equally spaced periods. They present characteristics such as level, noise, trend, seasonality, and outliers. Time series forecasting is the attempt to predict single or multiple future values. LÄS MER

4. 3. Artificial Neural Networks for Financial Time Series Prediction

   Master-uppsats, Stockholms universitet/Institutionen för data- och systemvetenskap

   Författare :Dana Malas; [2023]
   Nyckelord :artificial neural networks; time series analysis; deep learning; finance; long short-term memory; simple moving average;

   Sammanfattning : Financial market forecasting is a challenging and complex task due to the sensitivity of the market to various factors such as political, economic, and social factors. However, recent advances in machine learning and computation technology have led to an increased interest in using deep learning for forecasting financial data. LÄS MER

5. 4. CryptoCurrency Time Series analysis : Comparative analysis between LSTM and BART Algorithm

   Uppsats för yrkesexamina på grundnivå, Blekinge Tekniska Högskola/Institutionen för datavetenskap

   Författare :Lakshmi Vyshnavi Nerella; Chiranjeevi Ponnada; [2023]
   Nyckelord :;

   Sammanfattning : Background: Cryptocurrency is an innovative digital or virtual form of money thatuses cryptographic techniques for secured financial transactions within a decentralized structure. Due to its high volatility and susceptibility to external factors, itis difficult to understand its behavior which makes accurate predictions challengingfor the investors who are trying to forecast price changes and make profitable investments. LÄS MER

7. 5. Applying unprocessed companydata to time series forecasting : An investigative pilot study

   Kandidat-uppsats, Linnéuniversitetet/Institutionen för datavetenskap och medieteknik (DM)

   Författare :August Rockström; Emelie Sevborn; [2023]
   Nyckelord :Demand Forecasting; Irregular Time Series; Sales Prediction; Dataset Evaluation; Data Preprocessing;

   Sammanfattning : Demand forecasting for sales is a widely researched topic that is essential for a business to prepare for market changes and increase profits. Existing research primarily focus on data that is more suitable for machine learning applications compared to the data accessible to companies lacking prior machine learning experience. LÄS MER