Time series

Summary

In mathematics, a time series is a series of data points indexed (or listed or graphed) in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data. Examples of time series are heights of ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average. A time series is very frequently plotted via a run chart (which is a temporal line chart). Time series are used in statistics, signal processing, pattern recognition, econometrics, mathematical finance, weather forecasting, earthquake prediction, electroencephalography, control engineering, astronomy, communications engineering, and largely in any domain of applied science and engineering which involves temporal measurements. Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use o

Official source

https://en.wikipedia.org/wiki/Time_series

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Joint Fusion Learning of Multiple Time Series Prediction

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Accurate traffic density estimations is essential for numerous purposes like the developing successful transit policies or to forecast future traffic conditions for navigation. Current developments in the machine learning and computer systems bring the transportation industry numerous possibilities to improve their operations using data analyses on traffic flow sensor data . However, even state-of-art algorithms for time series forecasting perform well on some transportation problems, they still fail to solve some critical tasks. In particular, existing traffic flow forecasting methods that are not utilising causality relations between different data sources are still unsatisfying for many real-world applications . In this report, we have focused on a new method named joint fusion learning that uses underlying causality in time series. We test our method in a very detailed synthetic environment that we specially developed to imitate real-world traffic flow dataset. In the end, we use our joint-fusion learning on a historical traffic flow dataset for Thessaloniki, Greece which is published by Hellenic Institute of Transport (HIT) . We obtained better results on the short-term forecasts compared the widely-used benchmarks models that uses single time series to forecast the future.

2019

EPFL1999

A spatially explicit water temperature model for the river Wigger

Mattia Petar

Water temperature data are being collected at various locations in the Wigger for ecological purposes, namely to investigate health issues in sh populations. Such data need be linked to hydro-morphological and environmental features of the catchment in order to develop a spatially explicit model able to repro- duce and forecast the river's thermal regime. At the end of the project, the student will be familiar with time series processing and concepts in hydrologi- cal and environmental modelling. Optionally, the student may be involved the eldwork (1-2 days).

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