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Keywords
(16)
Artificial Intelligent
Autoregressive Model
Change Point Detection
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Load Balance
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APPLYING MACHINE LEARNING METHODS FOR TIME SERIES FORECASTING
APPLYING MACHINE LEARNING METHODS FOR TIME SERIES FORECASTING,Ben Choi,Raj Chukkapalli
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APPLYING MACHINE LEARNING METHODS FOR TIME SERIES FORECASTING
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Ben Choi
,
Raj Chukkapalli
This paper describes a strategy on learning from
time series data
and on using learned model for forecasting.
Time series
forecasting, which analyzes and predicts a variable changing over time, has received much attention due to its use for forecasting stock prices, but it can also be used for
pattern recognition
and data mining. Our method for learning from
time series data
consists of detecting patterns within the data, describing the detected patterns, clustering the patterns, and creating a model to describe the data. It uses a changepoint detection method to partition a
time series
into segments, each of the segments is then described by an autoregressive model. Then, it partitions all the segments into clusters, each of the clusters is considered as a state for a Markov model. It then creates the transitions between states in the
Markov model
based on the transitions between segments as the
time series
progressing. Our method for using the learned model for forecasting consists of indentifying current state, forecasting trends, and adapting to changes. It uses a moving window to monitor realtime data and creates an
autoregressive model
for the recently observed data, which is then matched to a state of the learned Markov model. Following the transitions of the model, it forecasts future trends. It also continues to monitor realtime data and makes corrections if necessary for adapting to changes. We implemented and successfully tested the methods for an application of load balancing on a parallel computing system.
Published in 2009.
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