Surveying future location and trajectory prediction methods for moving objects.
problem Growth of positioning technologies produces large tracking datasets.
method Extensive review of 50 works on predictive analytics for moving objects.
result Proposes a novel taxonomy of predictive algorithms.
Simple soft sensor models improve prediction accuracy with small moving windows.
problem Improving prediction accuracy in soft sensing processes with limited historical data.
method Five simple soft sensor methodologies with small moving windows were compared.
result Small moving window sizes led to the lowest prediction errors for all methods.
Deep neural networks predict winning moves in Othello, surpassing previous players.
problem Applying deep learning to Othello, a game with unique characteristics.
method Comparing CNN architectures and board encodings, training on extensive data, and evaluating move prediction accuracy and playing strength.
result Best CNNs predict winning moves in Othello and defeat previous players.
Paper predicts cryptocurrency bull and bear phases using Bitcoin's moving averages.
problem Determining cryptocurrency bull and bear phases based on Bitcoin performance.
method Employing predictive algorithms to forecast Bitcoin's 50 Day and 200 Day Moving Averages.
result Predicted data from Bitcoin's moving averages helps identify potential bull and bear phases.
Deep CNN model predicts stock prices with high accuracy.
problem Predicting stock prices due to volatility and multiple influencing factors.
method Deep CNN model trained on candle charts and continuous stock information.
result The method achieves 92.2% accuracy in predicting US NDAQ stock trends for 20 days.
The thesis explores prediction games with different opponents and moves, revealing intrinsic barriers and efficient algorithms.
problem Understanding and designing efficient algorithms for prediction games with various opponents and move orders.
method Geometric insights into three types of prediction games: general learning task, prediction with expert advice, and online convex optimization.
result Revealed intrinsic barriers and developed computationally efficient learning algorithms with strong theoretical guarantees.
Paper proposes an online adaptation algorithm for improving model performance.
problem Improving model fidelity in real-time for domain shift and time variance.
method Extended Kalman Filter with Exponential Moving Average and Dynamic Multi-Epoch strategy.
result Proposed algorithm outperforms existing methods in experiments.
In mathematics, a knot is a single strand of string crossed over itself any number of times, and connected at the ends. The Reidemeister Moves have been proven to be the three core moves necessary to fully untangle a knot. Some knots can be untangled to a loop (the unknot), while others are fundamentally knotted. We de…
Prediction markets can be manipulated by traders who can move contract settlements, harming price discovery.
problem Manipulation of settlement times in prediction markets leads to unfair wealth transfer and harms price discovery.
method Developed a model showing how settlement manipulation transfers wealth and harms price discovery, and observed real-world effects on Polymarket's Bitcoin contract.
result Manipulators capture significant profits from retail traders, especially when settlement times are short.
GPT learns a causal world model from token predictions, validated in game sequences.
problem Does GPT implicitly learn a causal world model from token predictions?
method Derived a causal interpretation of GPT's attention mechanism and proposed zero-shot causal structure learning.
result GPT can generate legal next moves with high confidence for sequences with encoded causal structures, but fails for illegal moves.
New method predicts spatio-temporal data with short and long-range dependence.
problem Uncertainty in predicting the distribution of mixed moving average fields.
method Theory-guided machine learning approach using generalized Bayesian algorithm.
result Fixed-time and any-time PAC Bayesian bounds for ensemble forecasts.
Improved tracking and prediction of moving objects in visual data streams.
problem Tracking and predicting multiple moving objects in visual data streams.
method Disentangled latent state-space model with amortized variational Bayesian inference.
result Significantly improved long-term prediction and object decomposition in the presence of occlusions.
Paper finds significant impact of stock market swings on equity risk premium predictability.
problem Predicting equity risk premium based on stock market behavior changes.
method Introduced Bullish Index and used FDMAA for returns analysis; considered 28 indicators.
result Positive shocks in Bullish Index correlate with strong equity risk premium predictability for up to six months, while negative shocks correlate for up to nine months.
Deep learning predicts road GHG emissions with speed, density, and past ERs.
problem Predicting GHG emissions from road networks to mitigate environmental impact.
method Developed a deep learning framework using LSTM networks with exogenous variables.
result LSTM with speed, density, GHG ER, and in-links speed from previous minutes performs best.
This research predicts vehicle movements by analyzing their intentions relative to road lanes.
problem Accurately forecasting vehicles' future movements for safe autonomous driving.
method LSTM networks with attention mechanisms applied to spatio-temporal graphs of road lanes.
result The model outperforms other state-of-the-art models in several metrics.
SQAIR generates videos of moving objects, tracking and predicting them reliably.
problem Generating videos of moving objects with reliable object tracking and prediction.
method Explicitly encoding object presence, locations, and appearances in latent variables.
result SQAIR reliably discovers and tracks objects throughout sequences of frames and generates future frames.
Optimizes prediction error method for time-varying models.
problem Achieving optimal prediction error rates for time-varying models.
method Nonlinear least squares method for time-varying parametric models.
result First rate-optimal non-asymptotic analysis for time-varying models.
Study uses neural processes to predict and classify crack patterns in moving disks.
problem Predicting and classifying crack patterns in moving disks.
method Peridynamic theory, Convolutional Neural Networks (CNNs), and Neural Processes.
result Neural Processes provide accurate predictions even with missing or insufficient data.
Study finds RNNs predict STBG better than ARIMA, useful for diabetes patients.
problem Improving short-term blood glucose prediction for diabetes management.
method Investigated Recurrent Neural Networks (RNNs) and compared them to ARIMA for STBG prediction.
result Population-based RNN model outperforms ARIMA across various prediction horizons.
Study uses open data to improve traffic emissions estimation.
problem Estimating accurate link level traffic emissions.
method Data-driven framework integrating MOVES, GPS, OSM, and satellite imagery.
result Neural network reduces RMSE by over 50% for key pollutants.
Enhances trading signals using image analysis and weighted moving averages.
problem Improving price trend trading strategies in financial markets.
method Image-induced importance weights applied to weighted moving averages of trading signals.
result Significant enhancement of price trend trading signals with improved portfolio selection.
Generates coherent 3D scenes from monocular videos without supervision.
problem Lack of 3D scene modeling in video generation models.
method Trains a model to generate 3D scenes with moving objects and a background from monocular videos.
result Trained model generates coherent 3D scenes with multiple moving objects and a background.
Research uses Sutte Indicator to predict stock market movements.
problem Predicting stock market movements accurately.
method Applied Sutte Indicator alongside SMA and MACD for comparison.
result Sutte Indicator shows better reliability in predicting stock movements.
Persistent Legendrian contact homology distinguishes knots using height functional.
problem Distinguishing Legendrian knots in R3. method Persistent homology applied to Chekanov-Eliashberg DGA, with height functional.
result Strong Morse inequalities for persistent Legendrian contact homology.
Wavelet and LSTM models improve stock price forecasting.
problem Predicting non-stationary stock prices.
method Wavelet Transform for denoising, SVR and LSTM for prediction.
result Improved accuracy in stock price predictions.
Deep learning models outperform traditional methods in stock price prediction.
problem Improving stock price prediction accuracy using deep learning.
method Comparative analysis of deep learning models (LSTM, GRU) and traditional methods (ARIMA, ARMA) on historical data.
result Deep learning models, particularly LSTM, outperform traditional methods in predicting stock prices across different time horizons.
A variation of the Minority Game has been applied to study the timing of promotional actions at retailers in the fast moving consumer goods market. The underlying hypotheses for this work are that price promotions are more effective when fewer than average competitors do a promotion, and that a promotion strategy can b…
A new knot move preserves pass-move equivalence and differs in count.
problem Defining and analyzing a new knot move.
method Introducing the 1-2-move and comparing it to pass-move and #-move. result Equivalence under 1-2-move for knots is equivalent to pass-move equivalence. Paper classifies local moves and shows how they affect knot transformations.
problem Classifying local moves and understanding their impact on knot transformations.
method Introduced an equivalence relation and binary relation on local moves, and used these to analyze knot transformations.
result Discovered that certain extended ST-moves can unknot a knot. Paper predicts house prices in major Chinese cities using LSTM networks.
problem Predicting house prices in major Chinese cities.
method Used LSTM networks compared to ARIMA for prediction accuracy.
result LSTM networks showed superior accuracy in predicting house prices.
It is well known that any two diagrams representing the same oriented link are related by a finite sequence of Reidemeister moves O1, O2 and O3. Depending on orientations of fragments involved in the moves, one may distinguish 4 different versions of each of the O1 and O2 moves, and 8 versions of the O3 move. We introd…
Minimal sets of moves for isotopic knots and trivalent graphs identified.
problem Identifying minimal sets of moves for isotopic knots and trivalent graphs.
method Provided and proved the existence of minimal generating sets of oriented Reidemeister moves for isotopic knots and spatial trivalent graphs.
result Twelve minimal generating sets of oriented Reidemeister moves for isotopic knots and ten for spatial trivalent graphs identified.
Study examines moving average trading rule's performance on stock indexes.
problem Linking trading rule performance to asset stochastic process.
method Calculated Sharpe ratio as a function of look-back period and asset distribution.
result Performance depends on autocorrelation vs. drift for different look-back periods.
Improved LSTM and ARIMA model for traffic flow forecasting.
problem Poor stability, high data requirements, and adaptability issues in existing traffic flow prediction methods.
method Combination prediction method based on improved LSTM and ARIMA models.
result The SDLSTM-ARIMA model achieves higher accuracy in traffic flow prediction.
ARMA nets expand receptive fields for dense prediction tasks.
problem Global information in dense prediction problems is challenging for traditional convolutional layers.
method ARMA layers with adjustable autoregressive coefficients replace traditional convolutions.
result ARMA networks improve dense prediction tasks including video prediction and semantic segmentation.
The paper explores different types of knot unknotting numbers using various local moves.
problem Investigating the unknotting numbers of knots using different local moves.
method Examined ribbon-move and pass-move on 2-knots and 1-knots, and high-dimensional-pass-move on high-dimensional knots.
result Found examples and bounds for various unknotting numbers associated with different local moves.
New statistical methods improve explainability of boosting models.
problem Uncertainty quantification for boosting models is computationally intensive and hard to interpret.
method Derive methods for statistical inference using gradient boosting and Boulevard regularization.
result Achieve asymptotically normal predictions with theoretical guarantees and runtime independent of data size.
The H(n)-move simplifies virtual and welded knots and links.
problem Tackling the unknotting of virtual and welded links.
method Extending the H(n)-move to virtual and welded links and showing their equivalence to Reidemeister moves.
result Virtualization and forbidden move can be realized by a finite sequence of generalized Reidemeister moves and H(n)-moves.
We prove that the classical set of moves for standard spines of 3-manifolds (i.e. the MP-move and the V-move) does not suffice to relate to each other any two standard skeleta of a 3-manifold with marked boundary. We also describe a condition on the 3-manifold with marked boundary that tells whether the generalised set…
Paper proves isotopic 2-knots can be transformed by cubulated moves.
problem Determining isotopy of 2-knots in 4D.
method Finite sequence of cubulated moves analogous to Reidemeister and Roseman moves.
result Two cubical links in 4D are isotopic if and only if one can pass to the other by cubulated moves.
Paper discusses the independence of Roseman moves involving triple points and tetrahedral moves.
problem The independence of Roseman moves including triple points and tetrahedral moves.
method Constructing new diagrams and proving the necessity of triple points and tetrahedral moves in sequences of Roseman moves.
result Roseman moves involving triple points and tetrahedral moves are independent.
Transformations of macroeconomic data affect machine learning forecasts, especially with regularization and nonlinearity.
problem The impact of data transformations on machine learning forecasts in macroeconomic contexts.
method Review and propose new data transformations, empirically evaluate their effects, and compare traditional and moving average rotations.
result Traditional factors should almost always be included as predictors, and moving average rotations can provide important gains.
Proves Yoshikawa eighth move's independence and finds minimal band moves.
problem Proving the independence of Yoshikawa eighth move and finding minimal generating set of band moves.
method Defining twisted diagrams and mirror cut surfaces, using surface-link groups.
result Proves independence of Yoshikawa eighth move and finds minimal generating set of band moves.
Classifies welded string links up to specific moves.
problem Classifying welded string links up to moves.
method Using Milnor invariants, classify welded string links up to 2n-move and Vn-move. result Classifies welded string links up to 2n-move and Vn-move. Classifies virtual links up to a specific move.
problem Characterizing 2-component virtual links using Ξ-moves. method Refined odd writhe and linking numbers.
result Classifies 2-component virtual links up to Ξ-moves. New rational band moves simplify knot classification.
problem Classifying knots using rational moves.
method Introduced oriented rational band moves and proved their effectiveness.
result Knots that can be unlinkified by rational moves are rationally slice.
New methods for delta-moves on algebraically split links identified.
problem Understanding delta-moves on algebraically split links.
method Introducing self and mixed delta-moves, proving equivalence, and calculating delta-splitting numbers.
result Two links are mixed delta-equivalent if they have the same pairwise linking number and components.
Improved estimation of VAR-Moving Average models using GLS.
problem Estimating VAR-Moving Average models efficiently.
method Use generalized least squares (GLS) for each fixed moving average polynomial.
result Likelihood function format similar to VAR models, allowing maximum likelihood estimation.