On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,181 papers · 148 categories

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4 results for “ADF-test”

Profitability of simple stationary technical trading rules with high-frequency data of Chinese Index Futuresq-fin.ST

Study on profitability of technical trading rules using high-frequency data of Chinese Index Futures.

problem Investigating the profitability of technical trading rules with high-frequency data of Chinese Index Futures.

method Converted MA, KDJ, and Bollinger bands into stationary processes and used ADF-test and SPA test to verify stationarity and assess trading rules' performance.

result Significant combinations of parameters for each indicator were found, but trading profits were eliminated with transaction costs included.

Chinese Index Futures Technical Trading Rules High-Frequency Data Stationary Processes ADF-test

2017-10-20abs ↗pdf ↗

Time Series Analysis of Big Data for Electricity Price and Demand to Find Cyber-Attacks part 2: Decomposition Analysiseess.SP

Paper analyzes electricity price and demand TSs using decomposition to detect cyber-attacks.

problem Detecting cyber-attacks in electricity price and demand time series data.

method Performed time series decomposition using additive and multiplicative methods, tested error term for patterns.

result Found a chance of cyber-attacks in the error term of decomposed TSs.

cyber-attacks time series analysis electricity price demand decomposition

2019-07-30abs ↗pdf ↗

Stock Price Prediction and Traditional Models: An Approach to Achieve Short-, Medium- and Long-Term Goalsq-fin.ST

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.

stock price prediction deep learning traditional models LSTM ARIMA

2024-09-29abs ↗pdf ↗

Forecasting Bitcoin closing price series using linear regression and neural networks modelsq-fin.ST

The paper forecasts Bitcoin prices using statistical and machine learning models.

problem Forecasting Bitcoin's daily closing prices.

method Used statistical SLR and MLR models, and machine learning MLP and LSTM neural networks.

result The proposed models outperformed benchmarks and demonstrated efficacy.

Bitcoin forecasting neural networks machine learning statistical models

2020-01-04abs ↗pdf ↗