12,172 research outputs found
Autoregressive Time Series Forecasting of Computational Demand
We study the predictive power of autoregressive moving average models when
forecasting demand in two shared computational networks, PlanetLab and Tycoon.
Demand in these networks is very volatile, and predictive techniques to plan
usage in advance can improve the performance obtained drastically.
Our key finding is that a random walk predictor performs best for
one-step-ahead forecasts, whereas ARIMA(1,1,0) and adaptive exponential
smoothing models perform better for two and three-step-ahead forecasts. A Monte
Carlo bootstrap test is proposed to evaluate the continuous prediction
performance of different models with arbitrary confidence and statistical
significance levels. Although the prediction results differ between the Tycoon
and PlanetLab networks, we observe very similar overall statistical properties,
such as volatility dynamics
Distributed ARIMA Models for Ultra-long Time Series
Providing forecasts for ultra-long time series plays a vital role in various
activities, such as investment decisions, industrial production arrangements,
and farm management. This paper develops a novel distributed forecasting
framework to tackle challenges associated with forecasting ultra-long time
series by utilizing the industry-standard MapReduce framework. The proposed
model combination approach facilitates distributed time series forecasting by
combining the local estimators of ARIMA (AutoRegressive Integrated Moving
Average) models delivered from worker nodes and minimizing a global loss
function. In this way, instead of unrealistically assuming the data generating
process (DGP) of an ultra-long time series stays invariant, we make assumptions
only on the DGP of subseries spanning shorter time periods. We investigate the
performance of the proposed distributed ARIMA models on an electricity demand
dataset. Compared to ARIMA models, our approach results in significantly
improved forecasting accuracy and computational efficiency both in point
forecasts and prediction intervals, especially for longer forecast horizons.
Moreover, we explore some potential factors that may affect the forecasting
performance of our approach
A Spatio-Temporal Point Process Model for Ambulance Demand
Ambulance demand estimation at fine time and location scales is critical for
fleet management and dynamic deployment. We are motivated by the problem of
estimating the spatial distribution of ambulance demand in Toronto, Canada, as
it changes over discrete 2-hour intervals. This large-scale dataset is sparse
at the desired temporal resolutions and exhibits location-specific serial
dependence, daily and weekly seasonality. We address these challenges by
introducing a novel characterization of time-varying Gaussian mixture models.
We fix the mixture component distributions across all time periods to overcome
data sparsity and accurately describe Toronto's spatial structure, while
representing the complex spatio-temporal dynamics through time-varying mixture
weights. We constrain the mixture weights to capture weekly seasonality, and
apply a conditionally autoregressive prior on the mixture weights of each
component to represent location-specific short-term serial dependence and daily
seasonality. While estimation may be performed using a fixed number of mixture
components, we also extend to estimate the number of components using
birth-and-death Markov chain Monte Carlo. The proposed model is shown to give
higher statistical predictive accuracy and to reduce the error in predicting
EMS operational performance by as much as two-thirds compared to a typical
industry practice
Short-Term Load Forecasting: The Similar Shape Functional Time Series Predictor
We introduce a novel functional time series methodology for short-term load
forecasting. The prediction is performed by means of a weighted average of past
daily load segments, the shape of which is similar to the expected shape of the
load segment to be predicted. The past load segments are identified from the
available history of the observed load segments by means of their closeness to
a so-called reference load segment, the later being selected in a manner that
captures the expected qualitative and quantitative characteristics of the load
segment to be predicted. Weak consistency of the suggested functional similar
shape predictor is established. As an illustration, we apply the suggested
functional time series forecasting methodology to historical daily load data in
Cyprus and compare its performance to that of a recently proposed alternative
functional time series methodology for short-term load forecasting.Comment: 22 pages, 6 Figures, 1 Tabl
Forecasting and Forecast Combination in Airline Revenue Management Applications
Predicting a variable for a future point in time helps planning for unknown
future situations and is common practice in many areas such as economics, finance,
manufacturing, weather and natural sciences. This paper investigates and compares
approaches to forecasting and forecast combination that can be applied to service
industry in general and to airline industry in particular. Furthermore, possibilities to
include additionally available data like passenger-based information are discussed
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