Energy price forecasting in the North Brazilian market using NN - ARIMA model and explanatory variables

2014 ◽  
Author(s):  
Jose Carlos R. Filho ◽  
Carolina M. Affonso ◽  
Roberto C. L. Oliveira
Keyword(s):  
Arima Model ◽  
Energy Price ◽  
Price Forecasting ◽  
Explanatory Variables ◽  
The North

scholarly journals Training subset selection in Hourly Ontario Energy Price forecasting using time series clustering-based stratification

2015 ◽  
Vol 156 ◽  
pp. 268-279 ◽  
Author(s):  
Karol Lina López ◽  
Christian Gagné ◽  
Germán Castellanos-Dominguez ◽  
Mauricio Orozco-Alzate
Keyword(s):  
Time Series ◽  
Subset Selection ◽  
Energy Price ◽  
Price Forecasting ◽  
Time Series Clustering ◽  
Training Subset

scholarly journals Price Forecasting of Mango in Varanasi Market of Uttar Pradesh

2018 ◽  
Vol 6 (2) ◽  
pp. 218-224 ◽  
Author(s):  
Ravishankar Pardhi ◽  
Rakesh Singh ◽  
Ranjit Kumar Paul
Keyword(s):  
Arima Model ◽  
Uttar Pradesh ◽  
The State ◽  
Percentage Error ◽  
Price Forecasting ◽  
Harvest Management ◽  
Alternate Bearing ◽  
Agricultural Produce ◽  
One Step ◽  
Forecasting Analysis

The study had been made to forecast the price of mango using ARIMA model in one of the major markets of Uttar Pradesh as the state ranks first position in production of mango in India. Varanasi market was selected purposively on the basis of second highest arrival market of mango in the state. Using ARIMA methodology on the monthly prices of mango collected from the Agricultural Produce Market Committee (APMC), Varanasi for the year 1993 to 2015. As the mango fruit having property of alternate bearing, only six month data from March to August was available in the market and accordingly had been used for forecasting analysis using E-views 7 software. The results revealed that the price in selected market was found to be highest during the start of the season using ARIMA (1,0,6) model, confirming the validity of model through Mean Absolute Percentage Error (MAPE). The MAPE was found to be less than 10 per cent for one step ahead forecast of year 2015. Forecasted price for the month of March was almost double than the price of other months. It indicates the necessity of adopting pre and post harvest management technologies for getting the benefit over increase in prices.

Dynamic factor analysis to estimate common trends in fisheries time series

2003 ◽  
Vol 60 (5) ◽  
pp. 542-552 ◽  
Author(s):  
A F Zuur ◽  
I D Tuck ◽  
N Bailey
Keyword(s):  
Time Series ◽  
Factor Analysis ◽  
Dynamic Factor Analysis ◽  
Dynamic Factor ◽  
Common Trends ◽  
Explanatory Variables ◽  
The North ◽  
The North Atlantic Oscillation ◽  
Aquatic Sciences

Dynamic factor analysis (DFA) is a technique used to detect common patterns in a set of time series and relationships between these series and explanatory variables. Although DFA is used widely in econometric and psychological fields, it has not been used in fisheries and aquatic sciences to the best of our knowledge. To make the technique more widely accessible, an introductory guide for DFA, at an intermediate level, is presented in this paper. A case study is presented. The analysis of 13 landings-per-unit-effort series for Nephrops around northern Europe identified three common trends for 12 of the series, with one series being poorly fitted, but no relationships with the North Atlantic Oscillation (NAO) or sea surface temperature were found. The 12 series could be divided into six groups based on factor loadings from the three trends.

Macrobenthic communities of the north-western Ross Sea shelf: links to depth, sediment characteristics and latitude

2010 ◽  
Vol 22 (6) ◽  
pp. 793-804 ◽  
Author(s):  
V.J. Cummings ◽  
S.F. Thrush ◽  
M. Chiantore ◽  
J.E. Hewitt ◽  
R. Cattaneo-Vietti
Keyword(s):  
Ross Sea ◽  
Fine Sand ◽  
Sediment Characteristics ◽  
Explanatory Variables ◽  
The North ◽  
Biogenic Habitat ◽  
Community Variability ◽  
North Western ◽  
Western Ross Sea ◽  
Ross Sea Shelf

AbstractIn early 2004 the Victoria Land Transect project sampled coastal north-western Ross Sea shelf benthos at Cape Adare, Cape Hallett, Cape Russell and Coulman Island from 100–500 m deep. We describe the benthic macrofaunal assemblages at these locations and, to assess the use of seafloor sediment characteristics and/or depth measures in bioregionalizations, determine the extent to which assemblage compositions are related to measured differences in these factors. Percentages of fine sand and silt, the ratio of sediment chlorophyllato phaeophytin, and depth were identified as important explanatory variables, but in combination they explained only 17.3% of between-location differences in assemblages. Consequently, these variables are clearly not strong determinants of macrofaunal assemblage structure. Latitudeper sewas not a useful measure of community variability and change. A significant correlation between both number of individuals and number of taxa and sediment phaeophytin concentration across locations suggests that the distribution of the benthos reflects their response to seafloor productivity. A number of factors not measured in this study have probably influenced the structure and function of assemblages and habitats. We discuss the implications of the results to marine classifications, and stress the need to incorporate biogenic habitat complexity into protection strategies.

scholarly journals Modelling New Zealand Road Deaths

2021 ◽  
Vol 32 (2) ◽  
pp. 4-15
Author(s):  
Colin Morrison ◽  
Ernest Albuquerque
Keyword(s):  
Time Series ◽  
New Zealand ◽  
Moving Average ◽  
Arima Model ◽  
Time Series Forecasting ◽  
Time Data ◽  
Baseline Model ◽  
Explanatory Variables ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag

New Zealand is developing an integrated road safety intervention logic model. This paper describes a core component of this wider strategic research carried out in 2018: a baseline model that extrapolates New Zealand road deaths to 2025. The baseline will provide context to what Waka Kotahi NZ Transport Agency is trying to achieve. It offers a way of understanding what impact interventions have in acting with and against external influences affecting road deaths and serious trauma. The baseline model considers autonomous change at a macro level given social and economic factors that influence road deaths. Identifying and testing relationships and modelling these explanatory variables clarifies the effect of interventions. Time-series forecasting begins by carefully collecting and rigorously analysing sequences of discrete-time data, then developing an appropriate model to describe the inherent structure of the series. Successful time-series forecasting depends on fitting an appropriate model to the underlying time-series. Several time-series models were investigated in understanding road deaths in the New Zealand context. In the final modelling an autoregressive integrated moving average (ARIMA) model and two differing autoregressive distributed lag (ARDL) models were developed. A preferred model was identified. This ARDL model was used to project road deaths to 2025.

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