scholarly journals On the Forecast Horizon of Magnetospheric Dynamics: A Scale‐to‐Scale Approach

2018 ◽  
Vol 123 (11) ◽  
pp. 9065-9077 ◽  
Author(s):  
Giuseppe Consolini ◽  
Tommaso Alberti ◽  
Paola De Michelis
Keyword(s):  
Forecast Horizon ◽  
Magnetospheric Dynamics

Minimal forecast horizon procedures for dynamic lot size models

1986 ◽  
Vol 33 (1) ◽  
pp. 111-122 ◽  
Author(s):  
Suresh Chand ◽  
Thomas E. Morton
Keyword(s):  
Lot Size ◽  
Forecast Horizon ◽  
Dynamic Lot Size

scholarly journals Remote-sensing magnetospheric dynamics with riometers: Observation and theory

2007 ◽  
Vol 112 (A5) ◽  
pp. n/a-n/a ◽  
Author(s):  
W. W. Liu ◽  
J. Liang ◽  
E. Spanswick ◽  
E. F. Donovan
Keyword(s):  
Remote Sensing ◽  
Magnetospheric Dynamics

scholarly journals Forecast horizon aggregation in integer autoregressive moving average (INARMA) models

Omega ◽  
2012 ◽  
Vol 40 (6) ◽  
pp. 703-712 ◽  
Author(s):  
Maryam Mohammadipour ◽  
John E. Boylan
Keyword(s):  
Moving Average ◽  
Autoregressive Moving Average ◽  
Forecast Horizon

scholarly journals Stock Price Prediction Using Convolutional Neural Networks on a Multivariate Time Series

2021 ◽  
Author(s):  
Sidra Mehtab ◽  
Jaydip Sen
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Stock Prices ◽  
Stock Price ◽  
Regression Models ◽  
Research Work ◽  
Hybrid Approach ◽  
Forecast Horizon ◽  
Stock Price Prediction ◽  
Price Prediction

Prediction of future movement of stock prices has been a subject matter of many research work. On one hand, we have proponents of the Efficient Market Hypothesis who claim that stock prices cannot be predicted, on the other hand, there are propositions illustrating that, if appropriately modelled, stock prices can be predicted with a high level of accuracy. There is also a gamut of literature on technical analysis of stock prices where the objective is to identify patterns in stock price movements and profit from it. In this work, we propose a hybrid approach for stock price prediction using machine learning and deep learning-based methods. We select the NIFTY 50 index values of the National Stock Exchange (NSE) of India, over a period of four years: 2015 – 2018. Based on the NIFTY data during 2015 – 2018, we build various predictive models using machine learning approaches, and then use those models to predict the “Close” value of NIFTY 50 for the year 2019, with a forecast horizon of one week, i.e., five days. For predicting the NIFTY index movement patterns, we use a number of classification methods, while for forecasting the actual “Close” values of NIFTY index, various regression models are built. We, then, augment our predictive power of the models by building a deep learning-based regression model using Convolutional Neural Network (CNN) with a walk-forward validation. The CNN model is fine-tuned for its parameters so that the validation loss stabilizes with increasing number of iterations, and the training and validation accuracies converge. We exploit the power of CNN in forecasting the future NIFTY index values using three approaches which differ in number of variables used in forecasting, number of sub-models used in the overall models and, size of the input data for training the models. Extensive results are presented on various metrics for all classification and regression models. The results clearly indicate that CNN-based multivariate forecasting model is the most effective and accurate in predicting the movement of NIFTY index values with a weekly forecast horizon.

scholarly journals Statistical Estimation of Modern Trends and Prospects of the Size and Structure Population of Ukraine

2017 ◽  
pp. 65-72
Author(s):  
Z. О. Palian ◽  
I. H. Bondarenko
Keyword(s):  
Population Size ◽  
Age Structure ◽  
Structural Changes ◽  
Age Groups ◽  
Natural Increase ◽  
Reproductive Age ◽  
Structural Factors ◽  
Forecast Horizon ◽  
The Crimea ◽  
Size And Structure

A balanced change in demographic processes should be considered as a prerequisite and, at the same time, as a result of the stable development of the state. Reproduction intensity depends not only on the character of demographic behavior, but also on the presence of contingents of the population, providing or potentially able to provide for its replacement. The dynamics of Ukrainian population, the transformation of its gender-age structure during the period of independence, taking into account the intensive and structural factors of natural increase and migration, is considered. During 2002-2015, the regime of survival and fertility improved in Ukraine, due to which the depopulation slowed down somewhat. But even these positive changes do not compensate for the loss of population size as a result of systematic aging, reducing the proportion of reproductive contingent and its aging. Significant demographic losses, direct and indirect, were caused by a hybrid war from Russia. Alienation of the territory of the Crimea and parts of Donbas is not only a minus 2.5 million citizens of Ukraine. This is a change in the structure of the population - a decrease in the proportion of older age groups that increase the demographic load and worsen the characteristics of survival and fertility of the maternal generation. In this work are presented the results of the short-term simulation of population size and structure taking into account modern trends of replacements components and existing administrative-territorial changes. Two scenarios of the forecast for 2018 have been developed, and the base year it was taken in 2013, when the Crimea was part of Ukraine. The first, realistic scenario was based on the preservation of the current situation - Ukraine without the annexed Crimea and the occupied part of the Donbas. The second scenario imitates the return to Ukraine of all the lost territories. Simulation showed that the population of Ukraine will be reduced by both scenarios, but to 41.9 million people under the scenario without the occupied and annexed territories and to 44.7 million people in the second scenario. The finish of war will due to slow down the death rate to 14.9%0. The age structure of the population does not differ significantly in two scenarios, because the forecast horizon is very short (4 years). The share of generation of parents and women of reproductive age in both variants of the forecast decreases. However, in the case of returning Crimea, it will be even lower (47.4% vs. 47.5% in the first scenario). The reason for this is the emigration of young and middle-aged people to the mainland of Ukraine and to the Russian Federation, which provided some preferences to the settlers from Ukraine. Expected structural changes combined with the modern life and fertility regime will worsen natural population growth rates in both scenarios. In further research is planned to build trend models of births and deaths that will allow the artificially restore the interrupted time series due to administrative-territorial incomparability of data on demographic events

scholarly journals Monetary Policy Report - January 2021

2021 ◽  
Author(s):  
◽  
◽  
◽  
◽  
◽  
...  
Keyword(s):  
Consumer Price Index ◽  
Fourth Quarter ◽  
Inflation Expectations ◽  
Forecast Horizon ◽  
Core Inflation ◽  
Expected Inflation ◽  
First Case ◽  
Downward Pressure ◽  
Domestic Prices ◽  
One Year

Macroeconomic Summary Overall inflation (1.61%) and core inflation (excluding food and regulated items) (1.11%) both declined beyond the technical staff’s expectations in the fourth quarter of 2020. Year-end 2021 forecasts for both indicators were revised downward to 2.3% and 2.1%, respectively. Market inflation expectations also fell over this period and suggested inflation below the 3% target through the end of this year, rising to the target in 2022. Downward pressure on inflation was more significant in the fourth quarter than previously projected, indicating weak demand. Annual deceleration among the main groups of the consumer price index (CPI) was generalized and, except for foods, was greater than projected in the October report. The CPI for goods (excluding foods and regulated items) and the CPI for regulated items were subject to the largest decelerations and forecasting discrepancies. In the first case, this was due in part to a greater-than-expected effect on prices from the government’s “VAT-fee day” amid weak demand, and from the extension of some price relief measures. For regulated items, the deceleration was caused in part by unanticipated declines in some utility prices. Annual change in the CPI for services continued to decline as a result of the performance of those services that were not subject to price relief measures, in particular. Although some of the overall decline in inflation is expected to be temporary and reverse course in the second quarter of 2021, various sources of downward pressure on inflation have become more acute and will likely remain into next year. These include ample excesses in capacity, as suggested by the continued and greater-than-expected deceleration in core inflation indicators and in the CPI for services excluding price relief measures. This dynamic is also suggested by the minimal transmission of accumulated depreciation of the peso on domestic prices. Although excess capacity should fall in 2021, the decline will likely be slower than projected in the October report amid additional restrictions on mobility due to a recent acceleration of growth in COVID-19 cases. An additional factor is that low inflation registered at the end of 2020 will likely be reflected in low price adjustments on certain indexed services with significant weight in the CPI, including real estate rentals and some utilities. These factors should keep inflation below the target and lower than estimates from the previous report on the forecast horizon. Inflation is expected to continue to decline to levels near 1% in March, later increasing to 2.3% at the end of 2021 and 2.7% at year-end 2022 (Graph 1.1). According to the Bank’s most recent survey, market analysts expect inflation of 2.7% and 3.1% in December 2021 and 2022, respectively. Expected inflation derived from government bonds was 2% for year-end 2021, while expected inflation based on bonds one year forward from that date (FBEI 1-1 2022) was 3.2%.

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