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# Autocorrelation time series Stata

### Learn About Time Series ACF and PACF in Stata With Data

In this guide, you will learn how to produce and plot an autocorrelation function (ACF) and a partial autocorrelation function (PACF) for a single time series variable in Stata using a practical example to illustrate the process. Readers are provided links to the example dataset and encouraged to replicate this example Testing for autocorrelation in a time series is a common task for researchers working with time-series data. We present a new Stata command, actest, which generalizes our earlier ivactest (Baum, Schaffer, Stillman, Stata Journal 7:4, 2007) and provides a more versatile framework for autocorrelation testing 10.2 - Autocorrelation and Time Series Methods One common way for the independence condition in a multiple linear regression model to fail is when the sample data have been collected over time and the regression model fails to effectively capture any time trends Correcting for Autocorrelation in the residuals using Stata. Serial correlation is a frequent problem in the analysis of time series data. Various factors can produce residuals that are correlated with each other, such as an omitted variable or the wrong functional form a past editor of the Stata Technical Bulletin and author of the Stata Press book Introduction to Time Series Using Stata. References Becketti, S. 1992.sts1: Autocorrelation and partial autocorrelation graphs. Stata Technical Bulletin 5: 27-28. Reprinte

Box-Pierce Test of autocorrelation in Panel Data using Stata. The test of Box & Pierce was derived from the article Distribution of Residual Autocorrelations in Autoregressive-Integrated Moving Average Time Series Models in the Journal of the American Statistical Association (Box & Pierce, 1970) When data set of interest is a time series data, we may want to compute the 1st-order autocorrelation for the variables of interest and to test if the autocorrelation is zero. One common test is Durbin-Watson test. The Durbin-Watson test statistic can be computed in proc reg by using option dw after the model statement

It's no big deal if you have only one time series, but if you have over 1,000, it's a drag. The pac command won't calculate the partial autocorrelation for lags beyond 4. Why not? Is this an arbitrary software limit, or is there some mathematical reason you can't calculate the lag-5 autocorrelation from a time series with T=12. Many thanks This series shows significant autocorrelation given that the p-value is less than 0.05. therefore, we can reject the null that all lags are not autocorrelated. The graphic view of the AC shows a slow decay in the trend, suggesting non-stationarity As it is not time-series data and there is no spatial effect, is it correct not to check for autocorrelation? Does a Durbin-Watson result of .23 mean something? regression autocorrelation. Share. Cite. Improve this question. Follow edited May 28 '14 at 7:35. Nick Cox

Time Series Analysis More usual is correlation over time, or serial correlation: this is time series analysis So residuals in one period (ε t) are correlated with residuals in previous periods (ε t-1, ε t-2, etc.) Examples: tariff rates; debt; partisan control of Congress, votes for incumbent president, etc. Stata basics for time series analysi Interrupted time series (e.g., Figure 1) is a special case of the time series design. The following is typically required of this design: A) The treatment/intervention must occur at a specific point in time, B) The series (outcome) is expected to change immediately and abruptly as a result of th

Some basic techniques to examine your time-series residuals for the presence of auto-correlation. We plot our residuals over time, estimate a simple AR(1) r.. The term autocorrelation refers to the degree of similarity between A) a given time series, and B) a lagged version of itself, over C) successive time intervals. In other words, autocorrelation is intended to measure the relationship between a variable's present value and any past values that you may have access to

Time series data is data collected over time for a single or a group of variables. For this kind of data the first thing to do is to check the variable that contains the time or date range and make sure is the one you need: yearly, monthly, quarterly, daily, etc. The next step is to verify it is in the correct format Time Series Autocorrelation for Panel Data with Stata (Panel) Stata time-series operator can be applied to panel data when both panel and time identifier are set with the xtset command. The model we estimates; We use the data Paneldata01 These notes largely concern autocorrelation Issues Using OLS with Time Series Data Recall main points from Chapter 10: Time series data NOT randomly sampled in same way as cross sectional—each obs not i.i.d Why? Data is a stochastic process—we have one realization of the process from a set of all possible realization Examining Trend With Autocorrelation in Time Series Data. In order to take a look at the trend of time series data, we first need to remove the seasonality. Lagged differencing is a simple. The results were consistent, indicating autocorrelation, if the order of autocorrelation is something like 1 or 2, it could easily be dealt with specifying an AR(1) or AR(2) model, however, as you can see below, the lags were way more significant than that

Microeconometrics using stata (Vol. 2). College Station, TX: Stata press.' and they indicate that it is essential that for panel data, OLS standard errors be corrected for clustering on the. Discover how to create correlograms and partial correlograms in Stata. Copyright 2011-2019 StataCorp LLC. All rights reserved Select Stat > Time Series > Time Series Plot, select price for the Series, click the Time/Scale button, click Stamp under Time Scale and select date to be a Stamp column. Select Stat > Time Series > Partial Autocorrelation to create a plot of partial autocorrelations of price. Select Calc > Calculator to calculate a lag-1 price variable Autocorrelation is a mathematical representation of the degree of similarity between a given time series and the lagged version of itself over successive time intervals. In other words, instead of calculating the correlation between two different series, we calculate the correlation of the series with an x unit lagged version (x ∈N ) of. Autocorrelation The correlation of a series with its own lagged values is called autocorrelation or serial correlation. The first autocorrelation of Y t is corr(Y t,Y First, let STATA know you are using time series data generate time=q(1959q1)+_n-1; _n is the observation no Introduction to Time Series Using Stata, Revised Edition, by Sean Becketti, is a first-rate, example-based guide to time-series analysis and forecasting using Stata. This is a must-have resource for researchers and students learning to analyze time-series data and for anyone wanting to implement time-series methods in Stata

1. Browse other questions tagged time-series autocorrelation stata unobserved-components-model or ask your own question. Featured on Meta Stack Overflow for Teams is now free for up to 50 users, foreve
2. TIME SERIES WITH STATA 0.1 Introduction This manual is intended for the ﬁrst half of the Economics 452 course and introduces 0.9 Correlogram and Partial Autocorrelation The corrgram command lists a table of autocorrelations, partial autocorrelations, and Qstatistics. It will also list a character-based plot of the autocorrelations an
3. Time Series on Stata: Forecasting by Smoothing. (ADF), which removes all the structural effects (autocorrelation) in the time series and then tests using the same procedure. The decision among which one of the 3 model to use is important for the size of the unit root test (the probability of rejecting the null hypothesis of a unit root when.

tsset—define data—Do this before work with other time-series based commands—I encourage you to read the manual entry on this. Example: tsset timevar. Time series operators. L.varname lags L2.varname 2nd lag . L(1/4).varname 1st through 4th lag. Example: reg yvar xvar L(1/2).xvar models yt = ( + (0xt + (1xt-1 + (2xt-2 + u Abstract. In this article, we consider time series OLS and IV regressions and introduce a new pair of commands, har and hart, which implement a more accu-rate class of heteroscedasticity and autocorrelation robust (HAR) F and t tests. These tests represent part of the recent progress on HAR inference. The F and

The value of d is close to 2 if the errors are uncorrelated. The distribution of d is reported by Durbin and Watson ().Tables of the distribution are found in most econometrics textbooks, such as Johnston and Pindyck and Rubinfeld ().The sample autocorrelation estimate is displayed after the Durbin-Watson statistic Create a time dependent series. 30 Run the Ljung-Box Q statistic and PAC (Partial autocorrelation) is shown in the Prob column. None of these residuals has any significant autocorrelation. The STATA command is : corrgram resid. 31 One can run Autoregression in the event of autocorrelation This can be done with newey y x1 x2 x3 lag(1) time.

Introduction to Time Series Analysis. Lecture 3. Peter Bartlett 1. Review: Autocovariance, linear processes 2. Sample autocorrelation function 3. ACF and prediction 4. Properties of the ACF 1. Mean, Autocovariance, Stationarity A time series {Xt} has mean function. issue may relate to the conditional mean of a series, or indeed to its variance or autocorrelation function. In this lecture, we consider several aspects of these two time-series properties, and present software tools that may be used in their evaluation If you're dealing with a large N, small T dataset and (-xtreg- is the Stata command you're going to use), -cluster()-ing standard errors on panel_id can manage both heteroskedasticity and autocorrelation. Otherwise, please come back to the list with more details What is Autocorrelation? Autocorrelation refers to the degree of correlation of the same variables between two successive time intervals. It measures how the lagged version of the value of a variable is related to the original version of it in a time series. Autocorrelation, as a statistical concept, is also known as serial correlation

In the earlier post, we looked at correlation of two time series. Autocorrelation is the correlation of a single time series with a lagged copy of itself. It's also called serial correlation. Often, when we refer to a series's autocorrelation, we mean the lag-one autocorrelation. So when using daily data, for example, the. Stata 10 (Time Series and Forecasting) StataCorp LP, College Station, TX. USD 1,795 (corporate), USD 985 (ed-ucational) for single user Stata/SE 10 (exact price varies by version and purchaser status). A review of Stata versions 9 and 10 time series Ch 12: Autocorrelation in time series data. In the previous chapters, errors $\epsilon_i$'s are assumed to be. uncorrelated random variables or; independent normal random variables. However, in business and economics, time series data often fail to satisfy above assumption Christopher F Baum & Mark E Schaffer, 2013. ACTEST: Stata module to perform Cumby-Huizinga general test for autocorrelation in time series, Statistical Software Components S457668, Boston College Department of Economics, revised 24 Jan 2015.Handle: RePEc:boc:bocode:s457668 Note: This module should be installed from within Stata by typing ssc install actest

A partial autocorrelation is a summary of the relationship between an observation in a time series with observations at prior time steps with the relationships of intervening observations removed. The partial autocorrelation at lag k is the correlation that results after removing the effect of any correlations due to the terms at shorter lags Assessing the memory of economic and financial data over time: the auto covariance and autocorrelation functions. Pure time series models of the mean: AR, MA, ARMA, ARFIMA models: introduction, dependence structure and the Box Jenkins methodology to choose the best model. Session 2: Estimation of AR(I)(FI)MA models in Stata

### 10.2 - Autocorrelation and Time Series Methods STAT 46

1. A review of Stata versions 9 and 10 time series and forecasting capability Stata 10 is a powerful, versatile, and exible statistical package with a wide range of user-friendly and accurate time series analytical and forecasting commands. It was released in June 2007, about two years after Stata 9 appeared. Prospective users want to know wha
2. Autocorrelation. Autocorrelation refers to the degree of correlation between the values of the same variables across different observations in the data. The concept of autocorrelation is most often discussed in the context of time series data in which observations occur at different points in time (e.g., air temperature measured on different days of the month)
3. The autocorrelation of a time series Y at lag 1 is the coefficient of correlation between Y t and Y t-1, which is presumably also the correlation between Y t-1 and Y t-2. But if Y t is correlated with Y t-1, and Y t-1 is equally correlated with Y t-2, then we should also expect to find correlation between Y t and Y t-2

Examining trend with autocorrelation in time series data. In order to take a look at the trend of time series data, we first need to remove the seasonality. Lagged differencing is a simple transformation method that can be used to remove the seasonal component of the series. A lagged difference is defined by Autocorrelation, also known as serial correlation, is the correlation of a signal with a delayed copy of itself as a function of delay. Informally, it is the similarity between observations as a function of the time lag between them. The analysis of autocorrelation is a mathematical tool for finding repeating patterns, such as the presence of a periodic signal obscured by noise, or identifying. Correlogram and Partial Correlogram with Stata (Time Series) Beside the formal unit root test ( ADf test and PP test ), the correlogram (or autocorrelation) and partial correlogram (or partial autocorrelation ) also can be used as graphical analysis to test whether our time series data are stationary or non-stationary An autocorrelation plot shows the properties of a type of data known as a time series. A time series refers to observations of a single variable over a specified time horizon. For example, the daily price of Microsoft stock during the year 2013 is a time series. Cross-sectional data refers to observations on many variables [ Stata Example. This example and discussion is shamelessly stolen pretty much verbatim from the Stata 12 Time Series Manual, pp. 323-324. I make no claims to original thinking or even original words here. It did take me a few minutes to cut and paste everything though Computing and interpreting the Durbin-Watson statistic. If e t is the residual given by = +, the Durbin-Watson statistic states that null hypothesis: =, alternative hypothesis , then the test statistic is = = =, where T is the number of observations. If one has a lengthy sample, then this can be linearly mapped to the Pearson correlation of the time-series data with its lags set up your data as a time-series (i.e., identify variable that represents time, or the sequential order of observations) is required by your software (e.g., tsset in STATA) do an OLS regression and test for autocorrelation of residuals (using Wald-Wolfowitz run test and Durbin-Watson test

Autocorrelation is a calculation of the correlation of the time series observations with values of the same series, but at previous times. But also, we remember: Partial Autocorrelation, on the other hand, summarizes the relationship between an observation in a time series with observations at previous time steps, but with the relationships of. When the autocorrelation is used to identify an appropriate time series model, the autocorrelations are usually plotted for many lags. Autocorrelation Example: Lag-one autocorrelations were computed for the the LEW.DAT data set. lag autocorrelation 0. 1.00 1. -0.31 2. -0.74 3. 0.77 4. 0.21 5. -0.90 6. 0.38 7. 0.63 8 autocorrelation is present in cross-section data as well as time-series data. In the cross-section data, the neighbouring units tend to be similar with respect to the characteristic under study. In time-series data, time is the factor that produces autocorrelation. Whenever some ordering of sampling units is present, the autocorrelation may. We can plot the autocorrelation function for a time series in R by simply not using the pl=FALSE argument: #plot autocorrelation function acf(x) The x-axis displays the number of lags and the y-axis displays the autocorrelation at that number of lags. By default, the plot starts at lag = 0 and the autocorrelation will always be 1 at lag = 0

An autocorrelation of +1 represents a perfect positive correlation (an increase seen in one time series leads to a proportionate increase in the other time series). An autocorrelation of negative. Posc/Uapp 816 Class 20 Regression of Time Series Page 3 3. In other words, the errors at time t, are created as a linear function of a random disturbance and ultimately of the first or original error, ε0 4. If we knew the value of the autocorrelation parameter, ρ, we would be in

### How can I compute Durbin-Watson statistic and 1st order

tocorrelations of the residual series, whereas the \Q statistic uses the unconditional auto-correlations. The \Q test can be applied to any time series as a test for \white noise, or randomness. For that reason, it is available in Stata as the command wntestq. This tes In the field of time series analysis, autocorrelation refers to the correlation of a time series with a lagged version of itself. For example, an autocorrelation of order 3 returns the correlation between a time series and its own values lagged by 3 time points.. It is common to use the autocorrelation (ACF) plot, also known as self-autocorrelation, to visualize the autocorrelation of a time. Chào các bạn, Trong bài viết hôm nay, mình sẽ giới thiệu tiếp về một hiện tượng/lỗi nữa cũng rất hay xảy ra trong hồi quy tuyến tính - đó là hiện tượng tự tương quan. Hiện tượng này thường xảy ra trong dữ liệu thời gian (time series) hoặc dữ liệu bảng (panel data) Time series regression for counts allowing for autocorrelation 33 2 Stata T echnical Bulletin STB-46 dm63 Dialog box window for browsing, editing, and entering observations

### Autocorrelation (ac and pac) - Statalist - The Stata Foru

ages oﬀering time-series tools. Interestingly, the conventions adopted by these programs for describing and analyzing time series vary widely, much more widely than the conven-tions used for cross-section techniques and classical hypothesis testing. Some of these packages focus primarily on time series and can be used on non-time-series question Testing for Autocorrelation Given time series data Y t = b 0 + b 1X t + u t t = 1, 2 T (1) the model may be subject to autocorrelation Accepted way of testing is to specify a functional form for the persistence (correlation) in the residuals over time and test to see whether this specification is statistically valid This autocorrelation of the residuals might not be a very good estimate of the autocorrelation of the true errors, especially if there are few observations and the independent variables have certain patterns. If there are missing observations in the regression, these measures are computed as though the missing observations did not exist

An autocorrelation plot is very useful for a time series analysis. This is because autocorrelation is a way of measuring and explaining the internal association between observations in a time series Time Series - Autocorrelation APS 425 - Advanced Managerial Data Analysis (c) Prof. G. William Schwert, 2001-2015 14 Try an ARMA(1,1) Model for Inflation •This is the best yet •AR coefficient is close to one, so we will also try differencing the inflation rate and then estimating an MA(1 Autocorrelation, time series and spatial analysis As we saw in Table 1‑3 , if we have a sample set { x i ,y i } of n pairs of data values the correlation between them is given by the ratio of the covariance (the way they vary jointly) to the square root of the variance of each variable Autocorrelation can also complicate the identification of significant covariance or correlation between time series (e.g., precipitation with a tree-ring series). Autocorrelation can be exploited for predictions: an autocorrelated time series is predictable, probabilistically, because future values depend on current and past values A time series containing records of a single variable is termed as univariate, but if records of more than one variable are considered then it is termed as multivariate. linear vs. non-linear A time series model is said to be linear or non-linear depending on whether the current value of the series is a linear or non-linear function of past.

### Time Series Tricks - ECONOMETRICS TUTORIAL for STAT

• Autoregression Intuition Consider a time series that was generated by an autoregression (AR) process with a lag of k. We know that the ACF describes the autocorrelation between an observation and another observation at a prior time step that includes direct and indirect dependence information
• When working with time series data, autocorrelation is the most common problem forecasters face. When the assumption of uncorrelated residuals is violated, we end up with models that have inefficient parameter estimates and upwardly-biased t-ratios and R 2 values
• Autocorrelation and partial autocorrelation plots are heavily used in time series analysis and forecasting. These are plots that graphically summarize the strength of a relationship with an observation in a time series with observations at prior time steps. The difference between autocorrelation and partial autocorrelation can be difficult and confusing for beginners to time series forecasting
• Sources of autocorrelation in time series regression data : There are many sources of auto-correlation in time series regression data. In many cases, the cause of autocorrelation is the failure of the analyst to include one or more important predictor variable in the model Checking for autocorrelation must be done carefully when some observations are missing from a time series or the time series is measured for independent groups. I show an approach where I pad the dataset with NA via tidyr::complete() to fill in any missed sampling times and make sure groups are considered independent prior to calculating the autocorrelation function Interrupted Time Series Analysis for Single Series and Comparative Designs: • Based on Stata program by Ariel Linden (2015) • Can perform single series or comparative ITS analyses • Will create all necessary dummy variables • Will adjust for autocorrelation (order needs to be determined before analysis) using Newey-.

### Autocorrelation, Durbin-Watson and non time-series dat

the errors be uncorrelated. Of course, this assumption can easily be violated for time series data, since it is quite reasonable to think that a prediction that is (say) too high in June could also be too high in May and July. That kind of cyclical eﬀect is indicative of positive autocorrelation, and it is quite common in time series data Series & Groups 5. Data Functions 6. Date Functions 7. Dummy Variables 8. Frequency Conversion 9. Basic Graphing 10. Statistical Analysis 11. Tables and Spools 12. Basic Estimation 13. Time Series Estimation 14. Forecasting 15. Programmin 11 Autocorrelation In time series data, Y t is typically correlated with Y t j, this is called autocorrelation or serial correlation The jthautocovariance=Cov( Y t; t j) can be estimated by Cov\(Y t;Y t j) = 1 T XT t=j+1 Y t Y j+1;T Y t j Y 1;T j Yj+1;T is the sample average of Y computed over observations t = j + 1;:::;T Y1;T j is the sample average of Y computed over observations t = 1;:::;T Autocorrelation in statistics is a mathematical tool that is usually used for analyzing functions or series of values, for example, time domain signals. In other words, autocorrelation determines the presence of correlation between the values of variables that are based on associated aspects. In a way, it is the cross-correlation of a signal with itself ### Stata Tutorial: Testing for Autocorrelation Pt

autocorrelation assumption. Everything here pertains to cross section data as well, not just time series. Suppose that Var(ut) depends on Xt:However we will still assume that each individual is drawn at random What about time (years, months, days, quarters, etc.) If you have one or both of the previous one you may need to control for variables that vary across time but not entities (like public policies) or variables that vary across entities but not time (like cultural factors)

Diagnosing autocorrelation using a correlogram. A correlogram shows the correlation of a series of data with itself; it is also known as an autocorrelation plot and an ACF plot.The correlogram is for the data shown above. The lag refers to the order of correlation.We can see in this plot that at lag 0, the correlation is 1, as the data is correlated with itself The book covers material taught in the Johns Hopkins Biostatistics Time Series Analysis course The partial autocorrelation function is a measure of the correlation between observations of a time series that are separated by k time units (y t and y t-k), after adjusting for the presence of all the other terms of shorter lag (y t-1, y t-2 y t-k-1) Types of time series data Properties of Time Series Data U.S. Monthly Presidential Approval Data, 1978:1-2004:7 OLS Strategies Properties of Time Series Data Number of Militarized Interstate Disputes (MIDs), 1816-2001 Number of Democracies, 1816-2001 Democracy-Conflict Example Nonstationarity in the Variance of a Series Properties of Time.

Working with Time Series in STATA tsset —define data—Do this before work with other time-series based commands—I encourage you to read the manual entry on this Example: tsset timevar Time series operators L. varname lags L2. varname 2 nd lag L(1/4). varname 1 st through 4 th lag Example: reg yvar xvar L(1/2).xvar models y t = + 0 x t + 1. One way to attempt to identify the appropriate specification for a time-series variable is to examine the autocorrelation function of the series, which is defined as ρs considered as a function of s. If the autocorrelation function declines exponentially toward zero, then the series might follow an AR(1) process with positive β1 ACTEST: Stata module to perform Cumby-Huizinga general test for autocorrelation in time series. Christopher Baum and Mark Schaffer () . Statistical Software Components from Boston College Department of Economics. Abstract: actest performs the general specification test of serial correlation in a time series proposed by Cumby and Huizinga (1990, 1992) ### Time Series Autocorrelation for Panel Data with Stata (Panel

A time series is a continuous sequence of observations on a population, taken repeatedly (normally at equal intervals) over time. In an ITS study, a time series of a particular outcome of interest is used to establish an underlying trend, which is 'interrupted' by an intervention at a known point in time 2.8 Autocorrelation. Just as correlation measures the extent of a linear relationship between two variables, autocorrelation measures the linear relationship between lagged values of a time series.. There are several autocorrelation coefficients, corresponding to each panel in the lag plot Autocorrelation and Partial Autocorrelation What Are Autocorrelation and Partial Autocorrelation? Autocorrelation is the linear dependence of a variable with itself at two points in time. For stationary processes, autocorrelation between any two observations depends only on the time lag h between them. Define Cov(y t, y t-h) = γ h.Lag-h autocorrelation is given b

### Autocorrelation in Time Series Data - DZone A

For all time series y, the lag 0 autocorrelation acf(1) = 1. lags — Lag numbers used for ACF estimation numeric vector. Lag numbers used for ACF estimation, returned as a numeric vector of length NumLags + 1. bounds — Approximate upper and lower confidence bounds numeric vector the median time point of the preintervention period to test for an interruption. In a suﬃciently long time series, the median time point of the preintervention period is a good choice of a pseudo-start period to maximize power to detect a signiﬁcant jump (because the subsample will be evenly split on both sides). For shorter time series Book Description. Introduction to Time Series Using Stata, Revised Edition provides a step-by-step guide to essential time-series techniques-from the incredibly simple to the quite complex- and, at the same time, demonstrates how these techniques can be applied in the Stata statistical package. The emphasis is on an understanding of the intuition underlying theoretical innovations and an. I'm using Stata 16 with register data from within an institutional offline computer so I won't be able to copy-paste Stata output here. Background: A couple of years back in time, I read in lots of tables from an sql server to work with in stata. Some files are called geodataYEAR.dta for some 30 years, and contain among other things the. What is autocorrelation plot in Python? Autocorrelation plots are a common tool used to check the randomness in a given data set.It is primarily used to do time series analysis and forecasting. It is used to summarize a relationship's strength with observation in a time series with observations at prior time steps graphically

A time series showing short-term autocorrelation together with its correlogram. Fig. 3 shows an alternating (negative correlation) time series. The coefficient r k alternates as does the raw data (r 1 is negative and r 2 is positive. For a random time series, autocorrelation function will show you how quickly it becomes unsimilar with itself, while periodic time series will show at what delay/lag values time series is similar with itself. Hope this isn't as confusing as it seems. Share. Cite. Follow answered Dec 3 '17 at 2:44 The time series z does not exhibit any clear pattern. In this exercise, you'll plot an estimated autocorrelation function for each time series. In the plots produced by acf(), the lag for each autocorrelation estimate is denoted on the horizontal axis and each autocorrelation estimate is indicated by the height of the vertical bars. Recall that. A positive (negative) autocorrelation means that an increase in your time series is often followed by another increase (a decrease). If the autocorrelation is close to 1, then an increase is almost certainly followed by another increase. In other words, the average value of the time series is increasing   • Pool hose Connector to skimmer.
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