Essays about: "neural network autoregression"
Found 5 essays containing the words neural network autoregression.
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1. Forecasting Monthly Swedish Air Traveler Volumes
University essay from Uppsala universitet/Statistiska institutionenAbstract : In this paper we conduct an out-of-sample forecasting exercise for monthly Swedish air traveler volumes. The models considered are multiplicative seasonal ARIMA, Neural network autoregression, Exponential smoothing, the Prophet model and a Random Walk as a benchmark model. READ MORE
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2. Neural Ordinary Differential Equations for Anomaly Detection
University essay from KTH/Matematisk statistikAbstract : Today, a large amount of time series data is being produced from a variety of different devices such as smart speakers, cell phones and vehicles. This data can be used to make inferences and predictions. Neural network based methods are among one of the most popular ways to model time series data. READ MORE
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3. Forecasting anomalies in time series data from online production environments
University essay from Linköpings universitet/Institutionen för datavetenskapAbstract : Anomaly detection on time series forecasts can be used by many industries in especially forewarning systems that can predict anomalies before they happen. Infor (Sweden) AB is software company that provides Enterprise Resource Planning cloud solutions. READ MORE
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4. Machine Learning for Forecasting Signal Strength in Mobile Networks
University essay from Linköpings universitet/Statistik och maskininlärningAbstract : In this thesis we forecast the future signal strength of base stations in mobile networks. Better forecasts might improve handover of mobile phones between base stations, thus improving overall user experience. Future values are forecast using a series of past sig- nal strength measurements. READ MORE
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5. The art of forecasting – an analysis of predictive precision of machine learning models
University essay from Uppsala universitet/Statistiska institutionenAbstract : Forecasting is used for decision making and unreliable predictions can instill a false sense of condence. Traditional time series modelling is astatistical art form rather than a science and errors can occur due to lim-itations of human judgment. READ MORE