Search results for "Moving average"
showing 10 items of 41 documents
Network-Wide Adaptive Burst Detection Depicts Neuronal Activity with Improved Accuracy
2017
Neuronal networks are often characterized by their spiking and bursting statistics. Previously, we introducedan adaptive burst analysis methodwhich enhances the analysis power for neuronal networks with highly varying firing dynamics. The adaptation is based on single channels analyzing each element of a network separately. Such kind of analysis was adequate for the assessment of local behavior, where the analysis focuses on the neuronal activity in the vicinity of a single electrode. However, the assessment of the whole network may be hampered, if parts of the network are analyzed using different rules. Here, we test how using multiple channels and measurement time points affect adaptive b…
PENERAPAN METODE SINGLE MOVING AVERAGE DAN EXSPONENTIAL SMOOTHING PADA USAHA ASRIE MODESTA
2020
This study aims to (1) analyze the number of demands for batik products in the second period of 2018. (2) To analyze the most appropriate forecasting method. (3) To analyze the forecasting of the first period in 2019 using the selected forecasting method.
 This reseach uses primary data and secondary data with data collection techniques using interviews, observation, and documentation. The analysis used is Single Moving Averages and Exsponential Smoothing. 
 The results of research in forecasting demand for batik products in 2019 with the Single Moving Average method are 3,936 units with Mean Absolute Deviation (MAD) of 632.5 units and Mean Square Error (MSE) of 693,718 units. An…
Burst analysis tool for developing neuronal networks exhibiting highly varying action potential dynamics
2012
In this paper we propose a firing statistics based neuronal network burst detection algorithm for neuronal networks exhibiting highly variable action potential dynamics. Electrical activity of neuronal networks is generally analyzed by the occurrences of spikes and bursts both in time and space. Commonly accepted analysis tools employ burst detection algorithms based on predefined criteria. However, maturing neuronal networks, such as those originating from human embryonic stem cells (hESC), exhibit highly variable network structure and time-varying dynamics. To explore the developing burst/spike activities of such networks, we propose a burst detection algorithm which utilizes the firing s…
Forecasting Electricity Consumption and Production in Smart Homes through Statistical Methods
2022
Abstract Over the last years, a steady increase in both domestic electricity consumption and in the adoption of personal clean energy production systems has been observed worldwide. By analyzing energy consumption and production on photovoltaic panels mounted in a house, this work focuses on finding patterns in electrical energy consumption and devising a predictive model. Our goal is to find an accurate method to predict electrical energy consumption and production. Being able to anticipate how consumers will use energy in the near future, homeowners, companies and governments may optimize their behavior and the import and export of electricity. We evaluated the ARIMA and TBATS statistical…
Next-Day Bitcoin Price Forecast
2019
This study analyzes forecasts of Bitcoin price using the autoregressive integrated moving average (ARIMA) and neural network autoregression (NNAR) models. Employing the static forecast approach, we forecast next-day Bitcoin price both with and without re-estimation of the forecast model for each step. For cross-validation of forecast results, we consider two different training and test samples. In the first training-sample, NNAR performs better than ARIMA, while ARIMA outperforms NNAR in the second training-sample. Additionally, ARIMA with model re-estimation at each step outperforms NNAR in the two test-sample forecast periods. The Diebold Mariano test confirms the superiority of forecast …
Revisiting the Profitability of Market Timing with Moving Averages
2017
In a recent empirical study by Glabadanidis (“Market Timing with Moving Averages” (2015), International Review of Finance 15(13):387–425), the author reports striking evidence of extraordinarily good performance of the moving average trading strategy. In this paper, we demonstrate that this “too good to be true” reported performance of the moving average strategy is due to simulating trading with look-ahead bias. We perform simulations without look-ahead bias and report the true performance of the moving average strategy. We find that, at best, the performance of the moving average strategy is only marginally better than that of the corresponding buy-and-hold strategy. In statistical terms,…
Dynamic Asset Allocation Strategies Basedon Unexpected Volatility
2014
The author documents that at the aggregate stock market level, unexpected volatility is negatively related to expected future returns, and positively related to future volatility. The author demonstrates how the predictive ability of unexpected volatility can be utilized in dynamic asset allocation strategies that deliver a substantial improvement in terms of risk-adjusted performance as compared to traditional buy-and-hold strategies. In addition, the author shows that active strategies based on unexpected volatility outperform the popular active strategy with a volatility target mechanism, and have some edge over the popular market timing strategy with a 10-month simple moving average rul…
A Mixture Multiplicative Error Model for Realized Volatility
2006
A multiplicative error model with time-varying parameters and an error term following a mixture of gamma distributions is introduced. The model is fitted to the daily realized volatility series of deutschemark/dollar and yen/dollar returns and is shown to capture the conditional distribution of these variables better than the commonly used autoregressive fractionally integrated moving average model. The forecasting performance of the new model is found to be, in general, superior to that of the set of volatility models recently considered by Andersen et al. (2003, Econometrica 71, 579--625) for the same data. Copyright 2006, Oxford University Press.
Revisiting the Profitability of Market Timing with Moving Averages
2016
In a recent empirical study by Glabadanidis ("Market Timing With Moving Averages" (2015), International Review of Finance, Volume 15, Number 13, Pages 387-425; the paper is also available on the SSRN and has been downloaded more than 7,500 times) the author reports striking evidence of extraordinary good performance of the moving average trading strategy. In this paper we demonstrate that "too good to be true" reported performance of the moving average strategy is due to simulating the trading with look-ahead bias. We perform the simulations without look-ahead bias and report the true performance of the moving average strategy. We find that at best the performance of the moving average stra…
Exchange Rate Volatility in the Balkans and Eastern Europe: Implications for International Investments
2016
Our paper’s objective is to study the volatility of exchange rates from the region that have not yet adopted the Euro and are not members of the Exchange Rate Mechanism II by considering the exchange rate regime and the implications of currency volatility for foreign capital flows. We model exchange rate volatility by using standard deviations of daily logarithmic changes in the exchange rates, rolling standard deviations, Hodrick-Prescott filters to detect the trends in volatility and ARIMA models. We find that currency volatility remains a strong issue for these countries and that central banks have attempted to manage it, particularly after the global financial crisis. Spikes in monthly …