Time series decomposition works by splitting a time series into three components: seasonality, trends and random fluctiation. To show how this works, we will study the decompose( ) and STL( ) functions in the R language.
Extracting Seasonality and Trend from Data: Decomposition Using R
Posted by Armando Brito Mendes | Filed under estatística, Investigação Operacional, lições, linguagens de programação, materiais ensino, materiais para profissionais
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Uma excelente descrição da decomposição clássica com Python e R.
Understanding Decomposition
Decompose One Time Series into Multiple Series
Time series decomposition is a mathematical procedure which transforms a time series into multiple different time series. The original time series is often split into 3 component series:
- Seasonal: Patterns that repeat with a fixed period of time. For example, a website might receive more visits during weekends; this would produce data with a seasonality of 7 days.
- Trend: The underlying trend of the metrics. A website increasing in popularity should show a general trend that goes up.
- Random: Also call “noise”, “irregular” or “remainder,” this is the residuals of the original time series after the seasonal and trend series are removed.
Tags: engenharia, inferência, otimização, previsão
NASA PCoE Datasets
Posted by Armando Brito Mendes | Filed under data sets, estatística
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12 tabelas de dados q podem ser úteis
PCoE Datasets
Overview
The Prognostics Data Repository is a collection of data sets that have been donated by various universities, agencies, or companies. The data repository focuses exclusively on prognostic data sets, i.e., data sets that can be used for development of prognostic algorithms. Mostly these are time series of data from some nominal state to a failed state. The collection of data in this repository is an ongoing process.
Publications making use of databases obtained from this repository are requested to acknowledge both the assistance received by using this repository and the donators of the data. This will help others to obtain the same data sets and replicate your experiments. It also provides credit to the donators.
Users employ the data at their own risk. Neither NASA nor the donators of the data sets assume any liability for the use of the data or any system developed using the data.
If you have suggestions concerning the repository send email to kai.goebel [at] nasa.gov Thank you and please come again.
Datasets
[Algae Raceway Data Set] [CFRP Composites Data Set] [Milling Data Set ] [Bearing Data Set] [Battery Data Set] [Turbofan Engine Degradation Simulation Data Set] [PHM08 Challenge Data Set] [IGBT Accelerated Aging Sata Set] [Trebuchet Data Set] [FEMTO Bearing Data Set] [Randomized Battery Usage Data Set] [Capacitor Electrical Stress Data Set]
Tags: data mining, engenharia
