scholarly journals Climate extreme variables generated using monthly time‐series data improve predicted distributions of plant species

Ecography ◽  
2021 ◽  
Author(s):  
S. B. Stewart ◽  
J. Elith ◽  
M. Fedrigo ◽  
S. Kasel ◽  
S. H. Roxburgh ◽  
...  
Keyword(s):  
Time Series ◽  
Plant Species ◽  
Time Series Data ◽  
Series Data ◽  
Climate Extreme ◽  
Monthly Time Series

scholarly journals WHICH VARIABLES PREDICT INDONESIA'S INFLATION?

2019 ◽  
Vol 22 (1) ◽  
pp. 87-102 ◽  
Author(s):  
Susan Sunila Sharma
Keyword(s):  
Time Series ◽  
Comparative Analysis ◽  
Inflation Rate ◽  
Time Series Data ◽  
Predictor Variables ◽  
Series Data ◽  
Macroeconomic Variables ◽  
Monthly Time Series ◽  
Out Of Sample ◽  
Exhaustive List

We use an exhaustive list of Indonesia’s macroeconomic variables in a comparative analysis to determine which predictor variables are most important in forecasting Indonesia’s inflation rate. We use monthly time-series data for 30 macroeconomic variables. Using both in-sample and out-of-sample predictability evaluations, we report consistent evidence of inflation rate predictability using 11 out of 30 macroeconomic variables.

Popularity Function Forecasts for the 2005 UK General Election

2005 ◽  
Vol 7 (2) ◽  
pp. 174-190 ◽  
Author(s):  
David Sanders
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Early Summer ◽  
Vote Share ◽  
General Election ◽  
Series Data ◽  
Liberal Democrats ◽  
Percentage Points ◽  
Monthly Time Series ◽  
Popularity Function

The article provides a set of contingent forecasts for the forthcoming UK general election. The forecasts are based on popularity function derived from monthly time series data covering the period 1997–2004. On most likely assumptions, the forecasts produce a clear Labour victory in the early summer of 2005, with the Liberal Democrats increasing their vote share by roughly four percentage points.

scholarly journals ​Changes in Prices and Arrivals of Major Oilseed Crops in APMC Amreli (Gujarat): An Empirical Analysis

2021 ◽  
Author(s):  
A.S. Dudhat ◽  
Pushpa Yadav ◽  
A.P. Prajapati
Keyword(s):  
Time Series ◽  
Empirical Analysis ◽  
Time Series Data ◽  
Price Volatility ◽  
Series Data ◽  
Price Model ◽  
Oilseed Crops ◽  
Monthly Time Series ◽  
Best Fit

The price volatility has been the main centre of attention for policy planners. This study therefore, aims to examine the changes in price and arrivals of major oilseeds of APMC, Amreli (Gujarat) analyzing monthly time series data of last twenty years. The findings emerged from the study revealed that the month wise and year wise highest changes were observed for the groundnut (semi spreading), followed by sesamum (white). Month wise severe changes were observed in the price of sesamum (white), while year wise severe changes were observed for the sesamum (black). On the basis of adjusted R2, price model of semi-spreading groundnut was found to be the best fit among all the models.

scholarly journals Applicability of Smoothing Techniques in Generation of Phenological Metrics of Tectona grandis L. Using NDVI Time Series Data

2021 ◽  
Vol 13 (17) ◽  
pp. 3343
Author(s):  
Ramandeep Kaur M. Malhi ◽  
G. Sandhya Kiran ◽  
Mangala N. Shah ◽  
Nirav V. Mistry ◽  
Viral H. Bhavsar ◽  
...  
Keyword(s):  
Time Series ◽  
Plant Species ◽  
Time Series Data ◽  
Series Data ◽  
Individual Plant ◽  
Smoothing Techniques ◽  
Modis Ndvi ◽  
Ndvi Time Series ◽  
Phenological Metrics ◽  
Year 2013

Information on phenological metrics of individual plant species is meager. Phenological metrics generation for a specific plant species can prove beneficial if the species is ecologically or economically important. Teak, a dominating tree in most regions of the world has been focused on in the present study due to its multiple benefits. Forecasts on such species can attain a substantial improvement in their productivity. MODIS NDVI time series when subjected to statistical smoothing techniques exhibited good output with Tukey’s smoothing (TS) with a low RMSE of 0.042 compared to single exponential (SE) and double exponential (DE). Phenological metrics, namely, the start of the season (SOS), end of the season (EOS), maximum of the season (MAX), and length of the season (LOS) were generated using Tukey-smoothed MODIS NDVI data for the years 2003–2004 and 2013–2014. Post shifts in SOS and EOS by 14 and 37 days respectively with a preshift of 28 days in MAX were observed in the year 2013–2014. Preshift in MAX was accompanied by an increase in greenness exhibiting increased NDVI value.LOS increased by 24 days in the year 2013–2014, showing an increase in the duration of the season of teak. Dates of these satellite-retrieved phenological occurrences were validated with ground phenological data calculated using crown cover assessment. The present study demonstrated the potential of a spatial approach in the generation of phenometrics for an individual plant species, which is significant in determining productivity or a crucial trophic link for a given region.

scholarly journals Determinant Of Sharia Bank Indonesia Rate With Financing And Deposit In Indonesian Islamic Banking Asset

2020 ◽  
Vol 1 ◽  
pp. 1-13
Author(s):  
Roikhan Mochamad Aziz ◽  
Adit
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Positive Influence ◽  
Least Square ◽  
Series Data ◽  
Islamic Banking ◽  
Ordinary Least Square ◽  
Monthly Time Series ◽  
Significant Positive Influence

This study aims to analyze the effect of bank certificates of Indonesia sharia (SBIS), financing to deposit ratio (FDR), and non-performing financing (NPF) against assets of Islamic Banking in Indonesia. The data used in this study are monthly time series data from the period from 2009 until 2013, published by Bank Indonesia of Indonesian Financial Statistics Report. The method of analysis used in this study is the Ordinary Least Square (OLS). The results of this study indicate that the variable certificates Indonesia sharia banks ((5.296593 and 0.0000), and financing to deposit ratio (5.512164 and 0.0000) had significant positive influence on Islamic banking assets in Indonesia. While non-performing financing variables (15.78500 and 0.0000) had significant negative towards Islamic banking assets in Indonesia.

scholarly journals Forecasting Bank Indonesia Currency Inflow and Outflow Using ARIMA, Time Series Regression (TSR), ARIMAX, and NN Approaches in Lampung

2020 ◽  
Vol 17 (2) ◽  
pp. 166-177
Author(s):  
Laila Qadrini ◽  
Asrirawan Asrirawan ◽  
Nur Mahmudah ◽  
Muhammad Fahmuddin ◽  
Ihsan Fathoni Amri
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Data Type ◽  
Series Data ◽  
Time Series Regression ◽  
Forecasting Methods ◽  
Monthly Time Series ◽  
Forecasting Method ◽  
Future Data

There are various types of data, one of which is the time-series data. This data type is capable of predicting future data with a similar speed as the forecasting method of analysis.  This method is applied by Bank Indonesia (BI) in determining currency inflows and outflows in society. Moreover, Inflows and outflows of currency are monthly time-series data which are assumed to be influenced by time. In this study, several forecasting methods were used to predict this flow of currency including ARIMA, Time Series Regression (TSR), ARIMAX, and NN. Furthermore, RMSE accuracy was used in selecting the best method for predicting the currency flow. The results showed that the ARIMAX method was the best for forecasting because this method had the smallest RMSE.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data
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