Evaluation and Optimal Calibration of Purchase Time Recommendation Services

Price Comparison Sites enable customers to make better – more informed, less costly – buying decisions through providing price information and offering buying advice in the form of prediction services. While these services differ to some extent, they are comparable regarding their prediction target and usually monitor every arbitrarily small price decrease. We use a large data set of daily minimum prices for 272 smartphones consisting of 198,560 daily price movements from a Price Comparison Site to show that the standard prediction setting is not optimal. A custom evaluation framework allows the maximization of the achievable savings by altering the calibration of the forecasting service to monitor changes that exceed a certain threshold. Additionally, we show that time series features calculated in a calibration period can be used to obtain precise out of sample estimates of the saving optimal forecasting setting

Time Series Event Forecasting in Consumer Electronic Markets using Random Forests

Consumers are price-sensitive and opportunistic about the place of purchase when buying electronic goods. However, services that advise customers on their purchase time decisions for those products are missing. Given the objective to provide a binary signal to customers to either wait or purchase immediately, classification algorithms are a direct methodological choice. Approaches like random forests allow for the derivation of a probability and class prediction but are usually not used in time series contexts. This is due to missing or time-invariant regressors and unclear prediction settings. We show how classification methods can be used to generate reliable predictions of price events and analyze if they are subject to common market dependencies. Pooling univariate random forests and enhancing them with multivariate features shows that our approach generates stable and valuable recommendations. Because dependency structures between products are transferable, multivariate forecasting increases accuracy and issues recommendations where univariate approaches fail

Investigation of spaceborne trace gas products over St Petersburg and Yekaterinburg, Russia, by using COllaborative Column Carbon Observing Network (COCCON) observations

This work employs ground- and space-based observations, together with model data, to study columnar abundances of atmospheric trace gases (XH$_2$O, XCO$_2$, XCH$_4$ and XCO) in two high-latitude Russian cities, St. Petersburg and Yekaterinburg. Two portable COllaborative Column Carbon Observing Network (COCCON) spectrometers were used for continuous measurements at these locations during 2019 and 2020. Additionally, a subset of data of special interest (a strong gradient in XCH4 and XCO was detected) collected in the framework of a mobile city campaign performed in 2019 using both instruments is investigated. All studied satellite products (TROPOMI, OCO-2, GOSAT, MUSICA IASI) show generally good agreement with COCCON observations. Satellite and ground-based observations at high latitudes are much sparser than at low or mid latitudes, which makes direct coincident comparisons between remote-sensing observations more difficult. Therefore, a method of scaling continuous Copernicus Atmosphere Monitoring Service (CAMS) model data to the ground-based observations is developed and used for creating virtual COCCON observations. These adjusted CAMS data are then used for satellite validation, showing good agreement in both Peterhof and Yekaterinburg. The gradients between the two study sites (ΔXgas) are similar between CAMS and CAMS-COCCON datasets, indicating that the model gradients are in agreement with the gradients observed by COCCON. This is further supported by a few simultaneous COCCON and satellite ΔXgas measurements, which also agree with the model gradient. With respect to the city campaign observations recorded in St Petersburg, the downwind COCCON station measured obvious enhancements for both XCH$_4$ (10.6 ppb) and XCO (9.5 ppb), which is nicely reflected by TROPOMI observations, which detect city-scale gradients of the order 9.4 ppb for XCH$_4$ and 12.5 ppb for XCO

Benchmarking CMIP5 models with a subset of ESA CCI Phase 2 data using the ESMValTool

The Coupled Model Intercomparison Project (CMIP) is now moving into its sixth phase and aims at a more routine evaluation of the models as soon as the model output is published to the Earth System Grid Federation (ESGF). To meet this goal the Earth System Model Evaluation Tool (ESMValTool), a community diagnostics and performance metrics tool for the systematic evaluation of Earth system models (ESMs) in CMIP, has been developed and a first version (1.0) released as open source software in 2015. Here, an enhanced version of the ESMValTool is presented that exploits a subset of Essential Climate Variables (ECVs) from the European Space Agency's Climate Change Initiative (ESA CCI) Phase 2 and this version is used to demonstrate the value of the data for model evaluation. This subset includes consistent, long-term time series of ECVs obtained from harmonized, reprocessed products from different satellite instruments for sea surface temperature, sea ice, cloud, soil moisture, land cover, aerosol, ozone, and greenhouse gases. The ESA CCI data allow 'extending the calculation of performance metrics as summary statistics for some variables and add an important alternative data set in other cases where observations are already available. The provision of uncertainty estimates on a per grid basis for the ESA CCI data sets is used in a new extended version of the Taylor diagram and provides important additional information for a more objective evaluation of the models. In our analysis we place a specific focus on the comparability of model and satellite data both in time and space. The ESA CCI data are well suited for an evaluation of results from global climate models across ESM compartments as well as an analysis of long-term trends, variability and change in the context of a changing climate. The enhanced version of the ESMValTool is released as open source software and ready to support routine model evaluation in CMIP6 and at individual modeling centers. (C) 2017 Elsevier Inc. All rights reserved.Peer reviewe

A time series based monitoring methodology to optimize purchase timing decisions

Businesses as well as consumers utilize price comparison portals prior to purchasing. Usually such systems provide price time series, but the transformation of the embedded information to select an appropriate purchase time is unknown. We present a methodology to forecast the probability of user customizable sufficient price change events. Four main methodological contributions are presented: (i) an economically meaningful definition of user specified price decreases, (ii) the modification of a bootstrap based ARIMA-GARCH volatility forecasting method to predict the probability of the defined events, (iii) the dynamic statistical evaluation of the forecasting accuracy and (iv) the measurement of the economic utility of the buying recommendation procedure using gain functions. Beyond this, the technique is applied to two distinct forecasting situations, which clearly show the dominance of the proposed decision theoretic framework in comparison to naive purchase strategies like always delaying or always buying immediately

ENHANCING PRICE ALERT RECOMMENDATION SERVICES - A COMPARATIVE STUDY

Both online shoppers and e-commerce retailers benefit from price aggregation platforms that reduce searching costs for consumers and marketing expenses for businesses. The business model of Price Comparison Sites requires customers to frequently revisit their site. Therefore, they offer a range of services that support users in all pre-purchase stages of the buying process. A frequently used offer is the price alert service that notifies users when a customer-specific threshold price is reached. However, users are not assisted with configuring this service to find the best trade-off between waiting time and the amount of the defined saving. We use a large data set with 110,230 daily price observations for electronic consumer goods to develop a method that predicts when price alarms are triggered. The presented algorithm combines approaches from multiple fields and extends time series forecasting methodologies with a bootstrapped forecasting ensemble to generate various price development scenarios. We systematically reduce the uncertainty in the bootstrapped path space by dynamically calibrating a customised decision criterion and generate 62,061 automated predictions. Our proposed approach not only outperforms the benchmark forecasting models significantly in terms of accuracy but also produces precise estimates in cases where traditional approaches fail

Should I buy my new iPhone now? Predictive Event Forecasting for Zero-Inflated Consumer Goods Prices

Price Comparison Sites enjoy great popularity because they enable customers to make better “ more informed, less costly “ buying decisions. We use a large dataset of daily minimum prices for 238 smartphones from Price Comparison Sites to develop a method

Current systematic carbon cycle observations and needs for implementing a policy-relevant carbon observing system

A globally integrated carbon observation and analysis system is needed to improve the fundamental understanding of the global carbon cycle, to improve our ability to project future changes, and to verify the effectiveness of policies aiming to reduce greenhouse gas emissions and increase carbon sequestration. Building an integrated carbon observation system requires transformational advances from the existing sparse, exploratory framework towards a dense, robust, and sustained system in all components: anthropogenic emissions, the atmosphere, the ocean, and the terrestrial biosphere. The goal of this study is to identify the current state of carbon observations and needs for a global integrated carbon observation system that can be built in the next decade. A key conclusion is the substantial expansion (by several orders of magnitude) of the ground-based observation networks required to reach the high spatial resolution for CO2 and CH4 fluxes, and for carbon stocks for addressing policy relevant objectives, and attributing flux changes to underlying processes in each region. In order to establish flux and stock diagnostics over remote areas such as the southern oceans, tropical forests and the Arctic, in situ observations will have to be complemented with remote-sensing measurements. Remote sensing offers the advantage of dense spatial coverage and frequent revisit. A key challenge is to bring remote sensing measurements to a level of long-term consistency and accuracy so that they can be efficiently combined in models to reduce uncertainties, in synergy with ground-based data. Bringing tight observational constraints on fossil fuel and land use change emissions will be the biggest challenge for deployment of a policy-relevant integrated carbon observation system. This will require in-situ and remotely sensed data at much higher resolution and density than currently achieved for natural fluxes, although over a small land area (cities, industrial sites, power plants), as well as the inclusion of fossil fuel CO2 proxy measurements such as radiocarbon in CO2 and carbon-fuel combustion tracers. Additionally, a policy relevant carbon monitoring system should also provide mechanisms for reconciling regional top-down (atmosphere-based) and bottom-up (surface-based) flux estimates across the range of spatial and temporal scales relevant to mitigation policies. The success of the system will rely on long-term commitments to monitoring, on improved international collaboration to fill gaps in the current observations, on sustained efforts to improve access to the different data streams and make databases inter-operable, and on the calibration of each component of the system to agreed-upon international scales.JRC.H.7-Climate Risk Managemen

Current systematic carbon cycle observations and needs for implementing a policy-relevant carbon observing system

A globally integrated carbon observation and analysis system is needed to improve the fundamental understanding of the global carbon cycle, to improve our ability to project future changes, and to verify the effectiveness of policies aiming to reduce greenhouse gas emissions and increase carbon sequestration. Building an integrated carbon observation system requires transformational advances from the existing sparse, exploratory framework towards a dense, robust, and sustained system in all components: anthropogenic emissions, the atmosphere, the ocean, and the terrestrial biosphere. The paper is addressed to scientists, policymakers, and funding agencies who need to have a global picture of the current state of the (diverse) carbon observations. We identify the current state of carbon observations, and the needs and notional requirements for a global integrated carbon observation system that can be built in the next decade. A key conclusion is the substantial expansion of the ground-based observation networks required to reach the high spatial resolution for CO2 and CH4 fluxes, and for carbon stocks for addressing policy-relevant objectives, and attributing flux changes to underlying processes in each region. In order to establish flux and stock diagnostics over areas such as the southern oceans, tropical forests, and the Arctic, in situ observations will have to be complemented with remote-sensing measurements. Remote sensing offers the advantage of dense spatial coverage and frequent revisit. A key challenge is to bring remote-sensing measurements to a level of long-term consistency and accuracy so that they can be efficiently combined in models to reduce uncertainties, in synergy with ground-based data. Bringing tight observational constraints on fossil fuel and land use change emissions will be the biggest challenge for deployment of a policy-relevant integrated carbon observation system. This will require in situ and remotely sensed data at much higher resolution and density than currently achieved for natural fluxes, although over a small land area (cities, industrial sites, power plants), as well as the inclusion of fossil fuel CO2 proxy measurements such as radiocarbon in CO2 and carbon-fuel combustion tracers. Additionally, a policy-relevant carbon monitoring system should also provide mechanisms for reconciling regional top-down (atmosphere-based) and bottom-up (surface-based) flux estimates across the range of spatial and temporal scales relevant to mitigation policies. In addition, uncertainties for each observation data-stream should be assessed. The success of the system will rely on long-term commitments to monitoring, on improved international collaboration to fill gaps in the current observations, on sustained efforts to improve access to the different data streams and make databases interoperable, and on the calibration of each component of the system to agreed-upon international scales.ISSN:1810-6277ISSN:1810-628