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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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Prediction interval of Y given AR model for log(Y) [duplicate]

I have been given an AR model with seasonal variation. \begin{equation} (1-\theta_1B)(1-\theta_2B^8)(log(Y_t)-\mu)=\epsilon_t \end{equation} Setting $X_t=log(Y_t)-\mu$ one gets the following \begin{...
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What are we trying to predict with ARIMA if we remove non-stationarity in data

I am beginning to learn time series analysis and I read that for ARIMA models, one needs to have a stationary process...that would mean removing periodicities and trends in the data....but isn't that ...
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How to calculate mean directional accuracy in R [on hold]

I have been searching for a method to calculate the mean directional accuracy in R. Basically I have a few forecasting arima models, and I would like to choose the best model. Besides looking at the ...
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auto.arima not working for seasonal=TRUE

I'm not sure whether I've just messed up something syntactically, if so I can't figure out what. I have the following data (res): I can run ...
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How to add the effect of structual change points (level shift, local time trends, changes in seasonal pulses ) in ARIMA IN PYTHON?

I am working on a time series forecasting problem which is described in details here. As I came to know that I was not considering structural changes and seasonal dummies and was building a simple ...
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How to find coefficients of ARMA model

What is the formula of the ARMA coefficients for AR(2) and MA(2)? What is exact formula of coefficients? I want make calculations manualy. I think we become more stupid because all calculations made ...
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Approximation in Impulse response calulation

I am dealing with the calculation of the impulse response functions in a VAR Model and I'm not sure I got it right. What I understand: The orthogonal Impulse Response function is a $MA(\infty)$ ...
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Integrated Random Walk is invertible and/or stationary?

I am wondering if the integrated Random Walk $y_t$ is Stationary and/or invertible $ y_t = 2y_{t-1} - y_{t-2} + e_t $ where $ e_t$ is a White Noise. To prove non stationarity I tried to say: If, by ...
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Why ARIMA/ARMA is performing very bad on out of sample (future) prediction?

I am working on a time series forecasting problem. The time series is - I plotted the rolling mean and rolling variance - rolling mean - and the rolling variance- The rolling mean/variance has not ...
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Intra-Day Time Series Forecasting w/ non-continuous data

I have 8 sample frames taken at the end of each quarter starting in 2017Q2 and ending in 2019Q1 for a stock ticker. Each sample frame consists of 2 trading days worth of stock data (open, high, low, ...
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Second-Order stationarity condition for complex-valued autoregressive process

Let $\{c_n\}$ be a complex-valued discrete autoregressive process of order $p$, $\mathsf{AR}(p)$, such that: \begin{equation} \label{cn} c_n = \sum\nolimits_{i=1}^{p}\rho_i c_{n-i} + w_n, \quad n \in (...
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Can I guess which time series model (ARIMA, SARIMA) I should use just by looking at the time series plot?

I have the time series plot shown above. Is is possible to know which model I should use solely by looking at this plot?
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Decomposition of time series vs ARIMA model

I am new to time series analysis, and I amm trying to understand the concepts behind this analysis. But I am still confused about the methods used to do such analyss, and what is the difference ...
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38 views

Negative values in time series forecast and high fluctuations in input data

I am trying to perform univariate time series forecasting in python on a monthly rainfall dataset of 136 years using ARIMA. My dataset is of the form: YEAR RAINFALL 2000-01-01 0 2000-...
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Forecasting a time series without trend and seasonality?

Can I use ARIMA methods to forecast a time series without any trend or seasonality? If no, are there any other methods that I can use to do such forecasting?
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Interpretation of ACF & PACF Plots & Confirm if series is stationary [on hold]

below are the ACF & PACf plots on my data; also pasted the output from ADF test on the data. My question: is my series stationary? Augmented Dickey-Fuller Test ...
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ARIMA predicts the one step ahead of the actual prediction

I have a time series dataset. When I build an ARIMA model, the model forecasts the values that are ahead of actual data points. What is wrong with my model. I used grid search to fit the model ARIMA (...
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ARMAX for Bitcoin prediction via sentiment

I am looking for correlation between social sentiment of BTC and the financial value of BTC. My approach is to use a time-series model to predict the bitcoin value at t+1 based on multiple input ...
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Long fitting times for SARIMAX. Fine tuning required for hourly data with weekly seasinality

I have developed a SARIMAX model for hourly data of two months aprox 1700 records and predecting a week data around 170 records. As the seasonality observed is for a week so m=168. Initially used GRID ...
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How to improve this time series model?

I am trying to fit a time series model for household data which is a time series variable. Initially my data looks like this, Since the data does not seem to be stationary I differenced the data. ...
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Decomposing Historical Data - Arimax versus linear regression

In the creation of a "marketing mix model", past sales data, is regressed against various marketing spend (TV, radio, billboards etc) along with other aspects influencing a companies sales such as ...
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What does this mean? Can you explain it to me in laymans term? [duplicate]

How to interpret this? What does the ARIMA(0,0,0)(0,1,0)[12] means in laymans term?
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How can i make constant variance for ARIMA model?

I want to fit ARIMA model to univariate time series. For this i took log then difference twice but the variance of the series is not constant. i also try with box.cox transformation but variance is ...
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How to calculate the coefficient of the model MA (1), with negative discriminant

In the calculation of the coefficient of the model MA(1) the following formula is used. For example, if the first value of acf=0.1923, the discriminant is D=B^2-4*A*C. D=0.85208 The solution has two ...
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Past observations & Error terms in GARCH and ARMA models

I am a bit confused concerning some of the "underlying concepts" of ARMA & GARCH models. I know that ARMA models are meant to forecast the conditional mean of a process, while GARCH models are ...
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Residuals unbiased but unbalanced

I have a couple different models I have been working with to predict order quantities for customers. One is a GLM, and one is an ARIMA model. For both models, the residuals sum to zero, but they are ...
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Comparing two time series statistically?

This question has been asked before with very good (but incomplete) answers. This and this are the two best answers that I found. But following is my doubt: Top answers from both (by IrishStat) the ...
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Should my time series be stationary to use ARIMA model

Should mytime series be stationary to use ARIMA model ? If yes, so why we do Integration in ARIMA ? I read some where that ARIMA can handle non stationary time series, what type of non ...
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ARIMA model forecast does't seem right

I am doing a SARIMA forecasting for my monthly data in STATA, and below is my forecast. I use a SARIMA (1,1,1)(0,1,1,12) model, but the forecast seem to only capture the previous month pattern, which ...
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Why ARIMA is prefered over any other time series analysis method

I am new to time series analysis, and I am self learner. I am using R language to learn how to do time series analysis. I started by studying the concepts and the theory behind such analysis, however ...
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SARIMA residual analysis question

I am new to ARIMA and here I have a question about residual analysis. My data is seasonal ARIMA data, and after fitting a non-seasonal difference and a seasonal difference to my data, I get the ...
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Interpretation of AR, MA, ARIMA models

In literature have read, that model: AR=> acf damped exponents and/or damped sine wave. pacf rare outputs beyond the confidence interval. The number of outputs per confidence interval in pacf ...
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Is statistical significance of a regressor important in forecasting different scenarios?

I have a good regression with ARMA errors (with exogenous variables), and it gives me reasonable forecasts. But now I need to construct different scenarios to forecast. My question is: Should I worry ...
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ARIMA generation of a time series

I'm trying to understand the ARIMA process by generating a sequence manually, then fitting an ARIMA model from statsmodel.tsa to check my results I've found an example there (not in english but doesn'...
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Interpreting accuracy values in ARIMA

How to interpet each measuring accuracy and how will i know if its accurate measure in the model
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SARIMA- determine order of difference

I have a question about seasonal ARIMA model to determine when to use seasonal difference only or use difference of seasonal difference. For example, I have some data that have both trend and seasonal ...
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1answer
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SARIMA help needed!

I have a data that seems to follow a seasonal pattern and trend, therefore I applied SARIMA in STATA to plot the acf and pacf. I used the first different of the seasonal difference (0,1,0)(0,1,0)12, ...
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Is this a SARIMA model?

I have some data which is monthly ship arrival numbers to port over the years. I am not sure is this data seasonal in nature? Do I need to apply the SARIMA instead of ARIMA in this case? Thank you!
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Time series ARIMA question

I am new to time series, and here i have a question about ARIMA- determine the order of MA and AR. In one post i learnt that usually we use the ACF and PACF plot to determine whether we should use a ...
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Specifying the bandwidth parameter for Local Whittle estimation

In every Local Whittle function I have seen I have to set bandwith parameter usually denoted m such that m = floor(1+T^delta). I am interested in the delta, how do I choose what value to put in the ...
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state-of-art of ARIMA

Please, could you advice me a good paper which talks about the ARIMA's state-of-art? I have already searched on google but I have not found anything interesting.
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Does SARIMA(3,1,18)(8,1,3) exist?

When I entered the above model in minitab to forecast, it said, 18 is not acceptable, and that value should be less than or equal to 5. I wonder whether it's a limitation of minitab, or this model is ...
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Adjusting for ARCH effect in regression analysis

I have the following regression: yt = b0 + b1X1t + b2X2t + b3X3t + e I then saw that e is serially correlated so I modeled the regression with ARIMA errors. However, then I saw that there was an ARCH ...
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The Importance of Initial Conditions in Autoregressive Modeling

I am developing an algorithm to classify time series by using autoregressive modeling. I have used the following two alternative methods, after fitting an AR(p) model to time series: Method 1: ...
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2answers
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OLS with ARMA errors, tip or two

I need a tip or two. I am performing OLS with dynamic factors (4x1 factors each representing a PANEL of 24 series, hence 4 time series). My OLS has autocorrelation in the error so I want to use OLS ...
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Lower ARIMA accuracy with BOX-COX

I'm using ARIMA model for time series forecast. My data has increasing variance and I applied a BOX-COX transformation to stabilise it. Here are charts: After I run my app it turned out that ARIMA ...
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How to implement a SARIMA-GARCH model?

I was reading "Prediction of daily peak electricity demand in South Africa using volatility forecasting models" by C. Sigauke and D. Chikobvu about electricity demand forecasting and I'm interested in ...
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What is the best calculation method to account for individual change, volatility, observation windows and time decays in time series data? ARIMA, ETS?

I am looking at applying a theoretical best calculation method to some particular time series (ts) data. Ideally the calculation method would encompass relative change in individual ts, volatility of ...
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How do I write a random walk with drift ARIMA? [closed]

I modeled oil prices and got the following coeffecients for my arima model with drift. Is this the right way of writing the model?