scholarly journals Policy Reforms and Productivity Change in the Dutch Drinking Water Industry: A Time Series Analysis 1980–2015

2019 ◽  
Vol 11 (12) ◽  
pp. 3463
Author(s):  
Jos L. T. Blank ◽  
Bert Enserink ◽  
Alex AS van Heezik
Keyword(s):  
Time Series ◽  
Drinking Water ◽  
Time Series Data ◽  
Break Point ◽  
Series Data ◽  
Productivity Change ◽  
Water Industry ◽  
Yardstick Competition ◽  
Policy Reforms ◽  
Annual Productivity

In the last four decades, the Dutch drinking water industry has undergone two major policy reforms, namely the consolidation of the industry by stimulating mergers and the introduction of yardstick competition by applying benchmarks. This paper addresses the question of whether these two instruments have improved productivity. Productivity changes are derived from an estimated cost function. The effects of average scale as well as the introduction of a form of yardstick competition on productivity are formally tested. Estimation is conducted on the basis of time series data in the period 1980–2015. Industry consolidation has taken place over a long period of time. Yardstick competition was introduced in 1997 on a voluntary basis. It shows that total factor productivity was rather stable in the period 1980–1998. Since 1998, annual productivity growth has been substantial (about 0.6% on average). There was an obvious break point in 1998, providing clear evidence that the introduction of the benchmark instrument has affected productivity change. Moreover, there are various indications that benchmarking has also contributed to improving quality and sustainability. We could not find any empirical evidence for the hypothesis that consolidation of the industry has improved productivity.

A Chaotic Prediction Method to Time Series Data

2010 ◽  
Vol 113-116 ◽  
pp. 1367-1370 ◽  
Author(s):  
Bin Sheng Liu ◽  
Ying Wang ◽  
Xue Ping Hu
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Time Series ◽  
Drinking Water ◽  
Time Series Data ◽  
Prediction Method ◽  
Drinking Water Quality ◽  
Series Data ◽  
Gray Model ◽  
Forecast Method

There are many ways to predict drinking water quality such as neural network, gray model, ARIMA. But the prediction precise is need to improve. This paper proposes a new forecast method according the characteristic of drinking water quality and the evidence showed that the prediction is effectively. So it is able to being used in actual prediction.

scholarly journals EFISIENSI, PRODUKTIVITAS DAN INDEKS KETIDAKSTABILAN PERIKANAN TUNA LONGLINE DAN PANCING TONDA (Efficieny, Productivity and Instability Index of Tuna Longline and Troll Line)

2016 ◽  
Vol 7 (1) ◽  
pp. 1
Author(s):  
Budi Wardono
Keyword(s):  
Time Series ◽  
Total Factor Productivity ◽  
Time Series Data ◽  
Malmquist Index ◽  
Series Data ◽  
Productivity Change ◽  
Instability Index ◽  
Factor Productivity ◽  
Important Indicator ◽  
Tuna Longline

<p>ABSTRACT<br /><br />Tuna longline and troll line are two dominant tuna fishing fleets in Palabuhanratu port. Tuna longline and troll line yielded around 7.06 thousand tons or 89.12 % of total fish production. The main problem of tuna industry was thing related to resource and capturing capacity. This study aimed to understand the capacity, efficiency, and total factor productivity of fisheries business of tuna in PPN Palabuhanratu, using Data Envelope Analysis (DEA) approach. The study was done in harbour area of Palabuhanratu, from January to March 2014. The time series data from 2010 to 2013 were obtained, covering the production of tuna longline and marine hook boats, input usage (boat, fuel, feed, fishermen, ice box, trip number, oil, water, capturing device). Under variable return to scale assumption, the result showed that business capacity of tuna in Palabuhanratu has been efficient. According to Malmquist approach, we found an important indicator of business productivity, ie. Index of total factor productivity change. Malmquist index of troll line was 0.851, while the Malmquist index of tuna longline was 1.139. Both indices showed the magnitude of productive change of the fleets. The annual change of total factor productivity could be described by the change of TFPCH from 2010 to 2013, the respective value of each year were 0.480; 1.945 and 1.023. Those showed the magnitude of productive change of fisheries business of tuna in PPN in Palabuhanratu.<br /><br />Keywords: DEA, efficiency, Malmquist index, productivity, troll line, tuna longline, VRS</p><p>-------<br /><br />ABSTRAK<br />Armada perikanan tuna longline dan pancing tonda merupakan armada yang dominan menangkap ikan tuna di Pelabuhan Perikanan Nusantara (PPN) Palabuhanratu.Total produksi dari keduanya sebanyak 7.066,64 ton (89,12%) dari total produksi ikan di Palabuhanratu. Permasalahan utama industri tuna adalah terkait sumber daya dan kapasitas penangkapan tuna. Tujuan penelitian ini untuk mengetahui tingkat efisiensi, perubahan total faktor produktivitas dan indeks ketidakstabilan usaha perikanan tuna dengan menggunakan tuna longline dan pancing tonda di Palabuhanratu dengan pendekatan Data Envelopment Analysis (DEA) dan Indeks Ketidakstabilan (Coppoct Instability Index). Penelitian dilakukan dikawasan PPN Palabuhanratu, Kabupaten Sukabumi, pada bulan Januari – Maret 2014. Data yang digunakan adalah data time series yang dikeluarkan oleh PPN Palabuhanratu dari tahun 2010-2013. Data yang digunakan dalam analisis ini meliputi produksi dari armada tuna long line dan pancing tonda. Adapun input yang digunakan adalah kapal (longline dan pancing tonda), BBM, umpan, nelayan, es, trip, oli, air, alat tangkap. Hasil analisis dengan asumsi variable return to scale (VRS), kapasitas usaha perikanan tuna di Palabuhanratu, pada armada tuna longline dan pancing tonda sudah efisien. Artinya bahwa sumber daya sudah dialokasikan secara efisien, penggunaan input dalam upaya penangkapan tuna sudah efisien. Hasil analisis menggunakan pendekatan indeks Malmquist diperoleh indeks total factor productivity change yang menunjukkan indikator penting produktifitas usaha. Nilai indeks Malmquist untuk amada pancing tonda sebesar 0,851 dan tuna longline sebesar 1,139, menunjukkan besarnya perbandingan perubahan produktivitas antara kedua armada tersebut. Perubahan total faktor produktivitas antar tahun digambarkan dari besarnya perubahan TFPCH dari tahun 2010 sampai dengan 2013 masing-masing besarnya 0,480; 1,945 dan 1,023, yang menunjukan perubahan besarnya produktivitas usaha perikanan tuna di PPN Palabuhanratu tahun 2010 sampai 2013.<br /><br />Kata kunci: DEA, efisiensi, Malmquist index, produktifitas, pancing tonda, tuna longline, VRS</p>

scholarly journals The Dynamic Shift Detector: an algorithm to identify changes in parameter values governing populations

2019 ◽  
Author(s):  
Christie A. Bahlai ◽  
Elise F. Zipkin
Keyword(s):  
Time Series ◽  
Case Studies ◽  
Time Series Data ◽  
Break Point ◽  
Small Sample ◽  
Series Data ◽  
Density Dependent ◽  
Parameter Values ◽  
Dynamic Shift

AbstractEnvironmental factors interact with internal rules of population regulation, sometimes perturbing systems to alternate dynamics though changes in parameter values. Yet, pinpointing when such changes occur in naturally fluctuating populations is difficult. An algorithmic approach that can identify the timing and magnitude of parameter shifts would facilitate understanding of abrupt ecological transitions with potential to inform conservation and management of species.The “Dynamic Shift Detector” is an algorithm to identify changes in parameter values governing temporal fluctuations in populations with nonlinear dynamics. The algorithm examines population time series data for the presence, location, and magnitude of parameter shifts. It uses an iterative approach to fitting subsets of time series data, then ranks the fit of break point combinations using model selection, assigning a relative weight to each break. We examined the performance of the Dynamic Shift Detector with simulations and two case studies. Under low environmental/sampling noise, the break point sets selected by the Dynamic Shift Detector contained the true simulated breaks with 70-100% accuracy. The weighting tool generally assigned breaks intentionally placed in simulated data (i.e., true breaks) with weights averaging >0.8 and those due to sampling error (i.e., erroneous breaks) with weights averaging <0.2. In our case study examining an invasion process, the algorithm identified shifts in population cycling associated with variations in resource availability. The shifts identified for the conservation case study highlight a decline process that generally coincided with changing management practices affecting the availability of hostplant resources.When interpreted in the context of species biology, the Dynamic Shift Detector algorithm can aid management decisions and identify critical time periods related to species’ dynamics. In an era of rapid global change, such tools can provide key insights into the conditions under which population parameters, and their corresponding dynamics, can shift.Author SummaryPopulations naturally fluctuate in abundance, and the rules governing these fluctuations are a result of both internal (density dependent) and external (environmental) processes. For these reasons, pinpointing when changes in populations occur is difficult. In this study, we develop a novel break-point analysis tool for population time series data. Using a density dependent model to describe a population’s underlying dynamic process, our tool iterates through all possible break point combinations (i.e., abrupt changes in parameter values) and applies information-theoretic decision tools (i.e. Akaike’s Information Criterion corrected for small sample sizes) to determine best fits. Here, we develop the approach, simulate data under a variety of conditions to demonstrate its utility, and apply the tool to two case studies: an invasion of multicolored Asian ladybeetle and declining monarch butterflies. The Dynamic Shift Detector algorithm identified parameter changes that correspond to known environmental change events in both case studies.

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

The Comparison of Land Subsidence between East and West Side of Bangkok, Thailand

2020 ◽  
Vol 17 (3) ◽  
pp. 1
Author(s):  
Angkana Pumpuang ◽  
Anuphao Aobpaet
Keyword(s):  
Time Series ◽  
Land Subsidence ◽  
Time Series Data ◽  
Series Data ◽  
Industrial Estates ◽  
Vertical Displacements ◽  
Land Deformation ◽  
Extensive Farming ◽  
Chao Phraya River ◽  
Set Up

The land deformation in line of sight (LOS) direction can be measured using time series InSAR. InSAR can successfully measure land subsidence based on LOS in many big cities, including the eastern and western regions of Bangkok which is separated by Chao Phraya River. There are differences in prosperity between both sides due to human activities, land use, and land cover. This study focuses on the land subsidence difference between the western and eastern regions of Bangkok and the most possible cause affecting the land subsidence rates. The Radarsat-2 single look complex (SLC) was used to set up the time series data for long term monitoring. To generate interferograms, StaMPS for Time Series InSAR processing was applied by using the PSI algorithm in DORIS software. It was found that the subsidence was more to the eastern regions of Bangkok where the vertical displacements were +0.461 millimetres and -0.919 millimetres on the western and the eastern side respectively. The districts of Nong Chok, Lat Krabang, and Khlong Samwa have the most extensive farming area in eastern Bangkok. Besides, there were also three major industrial estates located in eastern Bangkok like Lat Krabang, Anya Thani and Bang Chan Industrial Estate. By the assumption of water demand, there were forty-eight wells and three wells found in the eastern and western part respectively. The number of groundwater wells shows that eastern Bangkok has the demand for water over the west, and the pumping of groundwater is a significant factor that causes land subsidence in the area.Keywords: Subsidence, InSAR, Radarsat-2, Bangkok

Producers' Response to Changes in Prices and Marketing Policies: A Case Study of Sugarcane and Paddy in Eastern U.P. by S. C. Gupta and A. Majid. New York: Asia Publishing House, 1965.

1968 ◽  
Vol 8 (2) ◽  
pp. 308-309
Author(s):  
Mohammad Irshad Khan
Keyword(s):  
New York ◽  
Time Series ◽  
Time Series Data ◽  
Publishing House ◽  
Economic Incentives ◽  
The Other ◽  
Series Data ◽  
Poor Countries ◽  
Northern India

It is alleged that the agricultural output in poor countries responds very little to movements in prices and costs because of subsistence-oriented produc¬tion and self-produced inputs. The work of Gupta and Majid is concerned with the empirical verification of the responsiveness of farmers to prices and marketing policies in a backward region. The authors' analysis of the respon¬siveness of farmers to economic incentives is based on two sets of data (concern¬ing sugarcane, cash crop, and paddy, subsistence crop) collected from the district of Deoria in Eastern U.P. (Utter Pradesh) a chronically foodgrain deficit region in northern India. In one set, they have aggregate time-series data at district level and, in the other, they have obtained data from a survey of five villages selected from 170 villages around Padrauna town in Deoria.

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