Questions tagged [decomposition]

The tag has no usage guidance.

Filter by
Sorted by
Tagged with
1
vote
0answers
112 views

Neural Network regression on time series

I want to predict the trend values of a time serie [Y] based on the effect of other 10 input variables which can also have interaction. Since the combination of interaction between the inputs is ...
0
votes
0answers
22 views

Independent Component Regression using sklearn's FastICA

I am trying something along these lines ...
0
votes
0answers
32 views

Closest ARIMA models to Holt-Winter's Mixed Model and Time Series Decomposition Models

Can you please tell which ARIMA model will be closest to Holt-Winter's mixed model and Time Series Decomposition (additive/multiplicative) models And that ARIMA model maybe used in replacement of the ...
0
votes
1answer
33 views

Name for decomposition of joint probability into product of conditional probabilities

Consider a set of events $A_1, \dots, A_n$. By definition we have $$\mathbb{P}[A_1 \cap (A_2 \cap \dots \cap A_n)] = \mathbb{P} [(A_2 \cap \dots \cap A_n) | A_1] \mathbb{P}(A_1).$$ Applying the ...
0
votes
0answers
19 views

What is Effective Hypothesis Decomposition

My girlfriend used the program Statistica and in the ouput of ANOVA test, there is a graph with label "Effective Hypothesis Decomposition". We are lost what this means. There is even a help page on ...
1
vote
0answers
22 views

Decomposition random variable with conditional expectation [closed]

Why given some information set $I$, any random variable $x_t$ can be decomposed into the sum: $$x_t = E(x_t | I) + v_t $$ where $E(v_t | I) = 0$. I'm looking for a clear proof.
1
vote
2answers
50 views

Should I decompose time series before applying DTW

I have two time series which I want to compare by using the 'Dynamic Time Warping' technique. However, I am only interested in comparing the trend component of them (i.e. excluding the seasonal and ...
0
votes
1answer
70 views

Why doesn't the Wold's decomposition theorem imply a good AR(p) fit?

I'm trying to fit an AR(p) process to the standardized, 10 years long time series of monthly logreturns of a stock index and get extremely poor fit. I'm not surprised, because if I had a good fit, ...
0
votes
0answers
39 views

classical decomposition method(without seasonal component)

I understand the following points: When decomposing, the trend is obtained from the data by a regression line.=> T A cyclic component containing irregular variations is obtained(series/linear ...
3
votes
0answers
283 views

How to choose between additive and multiplicative decomposition in time series

I have a time series which is the number of weekly flu cases from 2010 to early 2018 in one county. I want to remove seasonality from my data so I can have a clearer data to infer the relationship ...
0
votes
0answers
34 views

Decomposition Difference Between Twitter and STL Method

I am having a lot of trouble understanding the difference between the two decomposition methods: twitter and stl. https://www.rdocumentation.org/packages/anomalize/versions/0.1.1/topics/...
0
votes
0answers
104 views

Anomalize Package time_decompose

I am trying to figure out what the meaning is behind each of the different compositions of the time_decompose function in the ...
3
votes
1answer
140 views

How to decide whether to model a time series additively or multiplicatively?

I've been reading a lot of theoretical content on additive and multiplicative decomposition, but when it comes to deciding what type of decomposition to use on my own data I find it kinda hard to do ...
0
votes
0answers
39 views

Observed very different from Trend, decomposed time series

using simple R lines, I have plots of decomposition of a time series. ...
2
votes
0answers
41 views

Should I include gender dummy for the probit regression in Heckman?

I'm estimating the wage equation for a selected sample which contains both males and females, afterwards which will be decomposed using the Oaxaca command in stata, by gender. The wage equation for ...
1
vote
1answer
105 views

If $X=Y+Z$ with known pdf of $X$, are $Y$ and $Z$ unique?

Say there are random variables such that $X=Y+Z$ with $Y$, $Z$ independent; knowing the pdfs of $Y$ and $Z$, one can (technically) find the pdf of $X$. Taking it from the other side: if one knows the ...
1
vote
0answers
30 views

How to decompose data into specific frequency-range components?

I have a 20 year daily wide area distributed weather data-set I would like to analyze as a part of my master thesis in engineering. Different frequency events impact my system in different ways and ...
1
vote
0answers
21 views

Is it common to decompose many time series in a business?

I am working in a industry. Most of the time I do statistical reports on sales. I'm new to time series analysis, so please be patient: the question might be obvious. I would like to monitor the sales ...
2
votes
1answer
349 views

Decomposing a time series with some zero values

There are many techniques to decompose a time series into trend, seasonal, and remainder components. I was wondering if these techniques can be applied without worry to time series which have some ...
2
votes
2answers
257 views

Time series forecasting and decomposition

I have a project about time series analysis. My data are not stationary and they have daily seasonality as shown in figure below. Is it correct to do the following steps? Decompose Time serie into ...
1
vote
1answer
53 views

3-way interaction not signficant; how to interpret subsequent 2-way interactions.

I know there are similar questions on this topic but I have checked all of them and haven't found the specific answer to my (simplistic) questions. There are 3 explanatory variables: A(3 levels, ...
1
vote
1answer
48 views

After decomposing my data, should I fit an ARIMA to the remainder or the trend?

I've decomposed my data to get rid of the seasonality. Now I want to use the arima function to fit an ARMA(p,q) model. Do I fit the model to the "random" component of the decomposition? That ...
4
votes
1answer
195 views

Even after seasonality adjustment, seasonality still remains. Why?

I'm trying the classic AirPassengers dataset in R and tried removing the seasonal component using deSeas <- forecast::seasadj(decompose(d,"multiplicative")), but ...
2
votes
1answer
126 views

Approximate AR(p) with a product of AR(1) and AR(2)

Literature suggests that any AR(p) ARIMA model can approximated as a combination of AR(1) and AR(2) processes. For example, one book suggests that an AR(3) model with the following coefficients: ...
0
votes
0answers
98 views

Bias variance dilemma (derivation in Haykin's Neural Networks)

I have a question regarding a certain derivation of the bias variance dilemma. Generally, I guess I have understood the derivation in, e.g., Geman's Paper, or in books like Bishop's Pattern ...
4
votes
1answer
178 views

Decomposition of single variable into mean and dispersion around mean

Consider a survey of firms of size $n$. This survey includes, among other variables, the average wage of workers in firm $i$ ($x_i$) and the number of workers in the firm ($L_i$). Both are random ...
2
votes
1answer
428 views

Seasonal trend loess (STL) decomposition around holidays

I am seasonally adjusting a weekly transaction time-series. The seasonal adjustment works great, except for on and around closed days. At and around these points there are serious errors. What is the ...
2
votes
1answer
114 views

Conditional expectation and variable decomposition

Suppose that $X$ and $Y$ have an uknown joint distribution $f_{XY}$. How can I formally demostrate that it always exists a unique decomposition of the form : $$ Y = E[Y|X] +\epsilon $$ without ...
1
vote
0answers
317 views

Determining Seasonality nature of a time series i.e additive or multiplicative

I have 4 years of data starting from 2014-Jan to 2017-Dec, I am trying to extract seasonality component from that series, to do this I am using decompose function in R, one of the parameters to ...
1
vote
1answer
22 views

Decompose growth of variable

This is probably an elementary question. Let's see. Say variable $y_t$ depends on two other processes, as follows: $$y_t=x_tz_t$$ I observe these variables in period 0 and period 1. I want to ...
0
votes
0answers
31 views

Seasonality for monthly period

The question is simple but I am having a trouble to figure it out. If I have a monthly data of 5 years how could calculate the seasonality for each month not quarter. I found this example online and ...
2
votes
0answers
138 views

Why does stl() decomposition require integer frequency?

I need to decompose and forecast weekly series with around 10 years of data. In this data leap years play an important role so I need the have non-integer frequency, frequency = (365.25/7) By ...
1
vote
0answers
82 views

Elimination of Trend and Seasonality Using Classical Decomposition Model

Brockwell and Davis (2016, page 26) have illustrated the classical decomposition model for elimination of trend and seasonality. the assumptions for this model are: $X_{t} = m_{t} + s_{t} + Y_{t}, ...
0
votes
1answer
1k views

How to perform variance decomposition?

suppose I have the variable X, and I simply want to know how much of the variance of X is explained by the other variables Y, Z and W. How can I do this in Stata? I have in mind a simple linear model, ...
1
vote
1answer
127 views

How to treat days when shops are closed in sales time series?

I am working on a time series that contains daily sales data. The aim of the project is to estimate the impact of marketing expenditure on the sales, while accounting for seasonality and trend. I ...
2
votes
0answers
99 views

How to decompose seasonality of a time series with a limited time span?

I am working on a time series that contains daily sales data over 2 and a half years. The aim of the project is to estimate the impact of marketing expenditure on the sales, while accounting for ...
0
votes
1answer
42 views

Can quintile/dispersion ratios be decomposed according to subgroups?

I'm try to decompose inequality/dispersion ratios (top 10% to bottom 10% or top 20% to bottom 20%) according to subgroups. I would like to say something like e.g. males are responsible for x% and ...
0
votes
2answers
259 views

TBATS Decomposition with High Residuals

I have an msts time series, hourly data of electricity prices that have daily, weekly and yearly seasonality. I am decomposing the data using TBATS. Data I am using covers 365 days. Residuals have ...
1
vote
0answers
56 views

What are the decomposition based forecasting methods?

I would like to learn that all forecasting methods are doing decomposition initially. For example, as I know, MAPA method is decomposing the time-series data first, then apply forecasting. If not all ...
4
votes
0answers
377 views

How to choose the best time window using structural times series with loess

My question is about the Cleveland et al. 1990 paper STL: A Seasonal-Trend Decomposition Procedure Based on Loess. The full citation is: Cleveland, RB, Cleveland, WS, McRae, JE, and Terpenning, I. ...
1
vote
0answers
62 views

Using one stock price as a predictor for another stock price

I have two stock prices with a strong trend which are non-stationary. I want to calculate the correlation between one stock say $x$ and another stock say $y$, but lagged 7 days back. Why should I ...
2
votes
1answer
2k views

Seasonal decomposition or Holt-Winters methods for forecasting?

When you have a time series that contains both trend and seasonal components, I learned that either seasonal decomposition (e.g., forecast the deseasonalized series, then add back the seasonal factor ...
1
vote
1answer
158 views

What would happen if we perform Time Series Decomposition on a data that only has trend and irregular components?

I am performing Decomposition of a Timeseries data. It appears that the data does not have a seasonal component and possess only trend and irregular components. However on still performing the ...
0
votes
1answer
110 views

Decompose Covariance by Observations

Suppose I observe $n$ iid realizations of two random variables $X$ and $Y$, denoted respectively $x_i$ and $y_i$. Observations can be groupped into two subsamples, with $n_1$ and $n_2$ observations. I ...
1
vote
1answer
4k views

How does loess decomposition work? [duplicate]

I actually read a lot about times eries and there I often read about "loess decomposition". But what is that exactly? It exists in package STL or packages about outlier-detection. Can someone give me ...
0
votes
0answers
236 views

R interpretation of STL() or decompose() results

How can I interpret the results of the decomposition of a time series, especially from the seasonal results? Example: ...
2
votes
0answers
380 views

Remove price and promotion effects from sales time series

I'm trying to measure the effect of an in-store media campaign on the sales. I have sales data along the time for test (treated) stores and control (not treated) stores. Before comparing test and ...
0
votes
1answer
1k views

Weekly and Monthly Decomposition of Daily Time Series

I have a data set including daily prices and demand of a commodity. I am sure that, price and demand weekly and monthly changing. So it has a seasonality effect. How can I decompose it by using daily ...
0
votes
1answer
58 views

decomposition of time series into many time series

I have two time series $x_t$ represents expected default rate from one data provider $y_t$ represent expected default rate from another data provider. Correlation between $y_t$ and $x_t$ is 0.9 ...
0
votes
1answer
106 views

Shift Share Decomposition for Non-Multiplicative Relationships

Given $$ Y_t = \sum_i \omega_{i,t} \cdot X_{i,t} $$ We can decompose the change in $Y$ from period $t$ to $t + 1$ into: $$ \begin{align} Y_{t + 1} - Y_t &= \left( \sum_i \omega_{i,t + 1} X_{i,...