Questions tagged [arima]

Refers to the AutoRegressive Integrated Moving Average model used in time series modeling both for data description and for forecasting. This model generalizes the ARMA model by including a term for differencing, which is useful for removing trends and handling some types of non-stationarity.

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30 views

Unusual few large spikes at pacf of arima residual model

I am using shampoo sales dataset which can be obtained from github. I fit the dataset using ARIMA$(5,1,0)$ and plot its residuals. The following are residual plots, acf and pacf. Question How to ...
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28 views

ARIMA(0,2,2) model - equation derivation

So on wikipedia here under Examples, it is mentioned that ARIMA(0,2,2) is given by: $$ X_t = 2X_{t-1} - X_{t-2} + (\alpha + \beta -2)\epsilon_{t-1} + (1 - \alpha)\epsilon_{t-2} + \epsilon_t \ \ \ \ \ ...
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ARMA Forecasting - Professional Work

I was curious how long does it take you to do ARMA forecasts in your professional environments? I'm getting started using the "Real Statistics" Add-On in Excel & I have only been familiar with ...
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24 views

Forecasting Sharp Peaks in a Time Series using Convolutional Neural Networks

I am having with me a time series data of a variable called Differential Pressure for some section in a natural gas refinery unit. Occurrences of sharp peaks in the variable value can possibly mean ...
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24 views

How are missing data handled in Time series estimation?

I am looking for most popular/theoretically sound methods for handling missing data in time series model (particularly ARMA class) estimation. Also what method is used in R (in arima and in forecast ...
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28 views

Prediction interval in time series for multi steps [on hold]

I want to calculate .95 prediction interval.I want to get the standard error of forecast also. Then I will use the formula - point forecast ± 1.96 * Standard error of forecast at that time t to get ...
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16 views

Code for best ARIMA Model using RMSE in Out Of Sample [closed]

Reading the book "Introductory Time Series with R" from Paul Cowperwait I found a code that would select the best arima model (similar to using the auto.arima function). So I am interested in ...
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1answer
27 views

What do these ACF and PACF plots tell you about AR and MA orders?

The residual of a seasonal_decompose (from python's statsmodels) yields the following ACF and PACF plots The sampling frequency of the time series is hourly, so the ACF plot hints at daily (24 ...
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Predicting daily targets based on monthly targets (Time series Problem) giving seasonality and lags

The problem at hand is that we have charity where fundraising campaigns are being run in which people pay with different payment methods and thus the money processing time varies (1-10 days) depending ...
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19 views

how to calculate safety stock from output of ARIMA model?

I have built an arima model using monthly sales as input suppose the output from ARIMA model is : How do we calculate safety stock for different lead times lead times (in days)?? ...
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Seasonal ARIMA- non stationarity after differencing and seasonal differencing

I am working with a seasonal time series, which is initially stationary. After many attempts, the best model that fits the data is an ARIMA(0,1,4)(0,1,1)[12]. However, checking for the stationarity of ...
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1answer
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CCF patterns intuition

I am studying pre whitening and cross correlation functions. I read somewhere... I forgot where, that there are patterns which tell you which lags of each variable to take. In this image, lags of X ...
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ARIMA GARCH NAGARCH model invertibility conditions

According to ARIMA model and GARCH model whose terms are in linear form, we can insist on invertibility conditions involving coefficients. But for instance in nonlinear asymmetric GARCH model(NAGARCH) ...
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Are Box-Cox and differencing redundant or complementary?

I was always under the impression that differencing and Box-Cox were two ways to achieve the same goal: Making a time series stationary so that it can be modeled using an ARMA process. However, ...
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Are residuals meaningful when using additive, robust, and regularization models on time series

If I am using additive models, regularization, or robust regression to model time series data, should I still check that the residuals have no autocorrelation and are stationary? I am under the ...
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1answer
26 views

Transfer function model with intevention that affects dependent and independent variables

How should one proceed when doing a transfer function model of a dependent Y and independent X, when an intervention affects both Y and X? I learned the order should be : Prewhiten X, Filter Y, ...
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1answer
27 views

Training ARIMA based on overlapping hourly weather forecasts

I am working with hourly water level data and I plan to forecast each day the next two weeks (on an hourly base meaning 14*24 = 336 forecasts each day). My regressor is an hourly weather forecast. ...
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1answer
28 views

Eacf table interpretation in R

I'm new to time series in R and have an assignment to identify the parameters for the AR and MA processes for a given time series, as well as to use eacf. Here are the results from the three functions:...
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1answer
44 views

How to interpret straight line as forecasting

I would like to make some short term forecasting using an AR(I)MA model. having the following daily time series, which is for the raw data: It seems to be like a white noise, based on the acf and ...
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20 views

Convert prediction for differenced Time Series ARIMA(1,0,1) and ARIMA (1,1,1)

I am working on a Time Series model, and the series appeared to be non-stationary (presence of trend). I tried 2 ways: 1) put original data into ARIMA(1,1,1) 2) manually difference first order ...
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Is it a valid claim, that by differencing a time series, it loses its memory, and as a result its predictive power?

Marcos Lopez de Prado seems to be a well known and renowned machine learning expert in the field of finance. I am very far from his level, as have not yet finished my PhD in economics, and only have ...
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1answer
41 views

Forecasting high-frequency electricity data with multiple seasonalities

for a school project I need to forecast high-frequency data using different methods of my choice. Data: I have hourly data on day-ahead electricity prices and a few other variables (hourly power ...
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2answers
60 views

ARIMA Model Residuals correlation in small sample?

I'm working with simulated data, trying to estimate the best ARIMA model for a time series consisting of 100 observations. This is the original data. First of all, I performed a combination of ADF ...
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MA(q) process invertibility condition derivation

Let's say that we have MA(q) process $$X_t= Z_t+ \theta_1*Z_{t-1} + .....\theta_q*Z_{t-q}$$ where $X_t$ are observed variables and $Z_t$ are errors. In order to ensure invertibility roots belong to $$...
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Does elastic net with Arima errors make sense?

I know of regression with arima errors, but can one also do elastic net regression with arima errors? I ask because I read somewhere that the residuals for elastic net are not really valid since ...
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ARIMA: no trend, no seasonality, so what is there to forcast? [duplicate]

I am very new to this topic. From what I understand, time series prediction with ARIMA requires that the series be series. So there is no seasonality, and no trend. In my limited understanding, ...
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2answers
23 views

ARIMA Minute Data and Holidays

I have a minutely dataset for a year duration. It has a daily seasonality. This would imply a seasonal period of 1440 according to https://robjhyndman.com/hyndsight/seasonal-periods/ . I thought of ...
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1answer
24 views

Including regressors to improve forecasts on white noise

I am conducting some time series forecasts using quite limited data, 13 years annually. Basically, I am trying to forecast companies emission totals using historical values. The historical data ...
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1answer
34 views

ARIMA for daily data over 5 years - forecast package

I have a question to auto.arima and seasonality. I have to analyze 39 single datasets which are prices of futures or equities. There are missing data which I replace with na.approx. Then I calculate ...
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How to forecast the future values ( test data) by making use of the fitted ARIMA model [duplicate]

I am tried as follows But problem is all the fitted values are 39 Data : Test and Train data
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17 views

How do i Remove Differencing applied to a time Series, ARIMA model?

Am trying to forecast using time series method called ARIMA. I have followed steps to build a time series model displayed in the code below. My challenge is on (Merging Actual and Forecast in One ...
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16 views

How to correctly utilize seasonal dummies

Suppose I am fitting a model with Arima errors. If there are no seasonal effects the model might be : $Y_t = \beta_0 + \beta_1 X_t + \epsilon_t$, where $\epsilon$ is an Arima process Say the data is ...
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35 views

Model Selection with Time Series Analysis

I'm new to time series and would like some help to determine the parameters for my analysis. I have minutely data for a few months and this is how a random week looks like. I ran the Augmented ...
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1answer
36 views

Fit and Predict Arima in R [closed]

I am trying to fit a Arima model in R with an independent variable (ARIMAX). The model fit data contains both positive and negative numbers. The issue is that after fitting the model, the predict ...
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1answer
51 views

trend stationary with external regressors

Suppose I have two trend - stationary time series with strong correlation. In the case where there are no regressors, if a time series is trend-stationary, it becomes stationary by subtracting a ...
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1answer
23 views

Relationship between weak and covariance stationary

I have read that the definition of weak stationary is : $ Mean(t) = mean(t + \tau)\\ Cov(t_1,t_2) = cov(t_1-t_2,0)\\ E[|x(t)|^2] < \infty $ In this definition of weakly stationary, can the ...
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Why am I getting better MAPEs when running an ARIMA model on a non-stationary time series (vs. a stationary one)?

I've been using ARIMA modelling to predict the number of orders a business receives. I have data for 3 years, and the time series shows a strong (uneven) upward trend, with increasing variance over ...
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Using Decomposition to Extrapolate seasonality, cycle and trends of predictors

I'm creating a dynamic regression model in which macroeconomic indicators are predictors/features in the model. I need to forecast these features n-steps into the future. I am planning to decompose ...
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19 views

Stationarity and invertibility conditions for ARMA(1, 1)

I am working on some homeworks and I have some troubles understanding one task regarding ARMA(1, 1) model and stationarity conditions: $y_t = Φy_{t-1} + ε_t + θε_{t-1}, ε_t ~ iidN(0, σ^2)$ What are ...
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1answer
44 views

SARIMA vs deseasonalizing with ARIMA [duplicate]

I am trying to check what is the differnce between modeling a time series with: SARIMA Deseasonalizing it first and then using ARIMA Is there a preferable method over the other? If yes, why ?
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1answer
54 views

ARIMA vs SARIMA

I am a self learner, and I am studying time series analysis. I came through the fact that ARIMA can be used to model a time series which is not stationary (Integrated ARMA model). The non ...
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Opposite results from the residuals and JB result test?

I`m trying to forecast some forex returns of currencies couples. I build up my ARIMA model and test for normality of distribution after the arima is applied. I get different results from the Jarque - ...
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1answer
66 views

ARIMA requires constant variance, so why can we use GARCH for its residuals?

According to what I have found so far, in order to implement ARIMA we need to have a stationary (constant mean and variance) transformed data set. In addition, I have also seen that the square of the ...
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1answer
27 views

Does it make sense to select SARIMAX parameters independently of covariates?

I have a vector of time series observations $y$ and a matrix of covariates $X$. I want to choose the best (in the sense of minimizing loss function, e.g. RMSE) $SARIMAX(p,d,q)×(P,D,Q)_{s}$ model ...
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1answer
44 views

Is this method to make data approximatly stationary valid?

I thought up this method to make data stationary for time series modeling with Arima. Does this method make any sense or is it completely flawed? For stationary data we need a constant mean and ...
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1answer
56 views

Home-brewing GARCH implementation

Motivation I want to wrap up my own GARCH implementation to make sure I have understood the underlying model/assumption. to leverage forecast::auto.arima to ...
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26 views

Help to interpret the ACF and PACF plots [duplicate]

I just exploring the sequential analysis with ARIMA (2-month data, period = 15 minutes, lags=360) I struggle with understanding the charts I receive after applying acf and pcf operations. My ...
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1answer
48 views

Non normal residuals at auto.arima [closed]

I´m doing an auto.arima to a time-serie and as a result, i get the model but with non normal residuals (jarque.bera.test). Can i use this model? Or it´s necessary to have normal residuals? Also for ...
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1answer
76 views

Estimating parameters in ARIMA

Without using auto.arima, what are the ways we can figure out what parameters we should use for modeling a time series data ? From the reference text here, it is mentioned that we cannot use ...
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1answer
52 views

BOX-COX TRANSFORMATION always stabilize variance

I am aware that box-cox transformation may make data set significantly normal distributed with constant mean and variance. But sometimes fails to convert data into normal. My question is even though ...