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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Time series model without ARMA component and with exogenous variables
I am trying to know what is the most simple model for time series data, with exogenous variables. What is the most simple framework I can use ? Is it possible to build a model more simple than ARIMAX, ...
3
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ARIMA Forecast is 14 Orders of Magnitude Higher than Training Data?
I am dealing with intermittent time series data, i.e. mostly zeros. Here is the particular time series that is giving me trouble:
[0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
60.0,
0.0,
0.0,
0.0,
0.0,
36.0,
0....
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answer
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Manually compute ARIMAX forecast
I need to forecast a phenomenon using an autoregressive model with an exogenous variable. I've estimated an ARIMA(1,0,0) model, but I can't understand how the forecasts are calculated.
Below is the ...
3
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1
answer
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Multiple predictions using ARIMA
I want to test the prediction capabilities of an sarima model for long sequences. I want to predict the next [24 48 96] datapoints and calculate the mse and rmse. Can you help me find bibliography and ...
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ARIMA - Identifying an outlier in residuals
I am trying to perform an ARIMA (SARIMAX in fact) and when looking at the residuals I see a large outlier. I am using python statsmodels.tsa.statespace.sarimax.
I ...
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SARIMAX.predict() and SARIMAX.forecast() exog? Does exog need to be preknown for predict()?
On SARIMAX.predict, when you have an exog but the exog is only known today and in the past, how do you predict the endog's next 12 months off just the exog and data ...
- 13
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Role of `trend` argument compared to integral order in ARIMA model
I am currently studying ARIMA models. When I checked for a Python library to train one, I stumbled upon statsmodels which features ARIMA (and SARIMAX from which ...
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Understanding the differences between auto_arima and SARIMAX predictions
I would like to better understand the differences between the results obtained with the SARIMAX function and the auto_arima function.
In particular, I would like to understand.
why the two models ...
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How is forecasting values for stationary time series even possible?
Forecasting future values for a time series using Holt-Winter's model makes a lot of sense: by identifying a trend and some seasonal patterns which are likely to repeat in the future one can make ...
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Do I need to take the residuals of the ARMA fit for my linear regression?
I am not used to time series econometrics. If I understand well, before conducting a linear regression (the most basic possible), we need to ensure the stationarity of our data - particularly with (...
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How to determine the theoretical ACF of a SARIMA model?
I need to determine the ACF for a SARIMA(0,0,1)(0,0,1)12 model with $\Theta=0.7$ and $\theta=0.4$. However, I am unsure what parameters to use.
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Forecasted value of ARMA model with order (p=1, d=0, q=1) scaled down and bias [duplicate]
I trained an ARMA model using Python's from statsmodels.tsa.arima.model import ARIMA .
Separated the training and testing data, and fitted the model with parameters ...
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Predicting quantiy sold using Time series data
I am struggling with a time series dataset comprising 12 features, including quantity sold and weather data, totaling approximately 1800 values, where data is recorded on a daily basis. My goal has ...
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fitting an ARMAX model with box constraints in python
I would like to identify some parameters from real world data (actually time series). After some mathematical manipulations of some equations describing my system, I conveniently end-up having a ...
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VECM model unable to predict approximately but able to learn the pattern
In the below image VECM model has learnt the pattern but did not predict properly there is a difference in actual and prediction
Have used the below dataset to predict the meanpressure:-
https://www....
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Specifying parameters for SARIMAX model with significant ACF / PACF at tails
I have hourly data that has a period of 1 day or 24 hours / time steps and I hope to do short term forecasting for a few days in advance. The ACF of the raw time series was periodic (see last figure) ...
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ARIMA parameter estimation from scratch
I am implementing ARIMA from scratch, and I am trying to understand how to estimate the parameters by the MLE + innovations algorithm approach. The likelihood is given by (as in Shumway and Stoffer: ...
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seasonal_order in statsmodels.tsa.statespace.sarimax.SARIMAX for daily data
I'm trying to understand how to set s for the seasonal order in the context of statsmodels.tsa.statespace.sarimax.SARIMAX. Per the documentation:
s is an integer ...
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Time Series Prediction with Partial Future Data (Presales)
I have a data set that contains tickets sold up to a certain point in time, and then presold tickets for future events. Just for demonstrative purposes, I'll use the Australian total wine sales data ...
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Linear Regression vs. SARIMAX with Exogenous Variable: Coefficient Interpretation
I have data concerning, say, ice cream sales and wish to predict future sales and also, to quantify the relationship between temperature and sales. The data has daily seasonality.
...
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ARIMAX with Exogenous Variable modeled using Ornstein-Uhlenbeck Process
I have a question with regard to building ARIMA with Exogenous Variables (ARIMAX) models where one of the Exogenous Variables is to be modeled using the Ornstein-Uhlenbeck process.
I recently read an ...
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Dealing with conditional heteroskedasticity in dynamic linear model
I'm working on a regression problem and am struggling to figure out how to deal with the conditional heteroskedasticity present in the error terms. I will try and work through what I have done so far, ...
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A small part of my data is non-stationary, how to solve the issue?
For a project, I have to produce an ARIMA forecast of Boeing's stock returns over the last 10 years. I've tried to check stationarity of data with ACF first and found out that lag 1 is statistically ...
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Why does my GLARMA model fail to converge when I exclude the independent variables, while a similarly structured one does not?
For purposes of validating a forecast model, I'd like to compare a GLARMA model that I developed to a null model that includes the same autocorrelation effects but lacks the environmental data. GLARMA ...
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SARIMA Models How to know if my D is 0, or no? [duplicate]
In ARIMA model i know if my d is different of 0 if the time series is not stationary. But in SARIMA model how could i know if my D is different of 0?
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Forecasting in ARIMA (python)
I have a time-series data (Date and Total). This is actual data from the past 2 years.
I understand how to pick the (p, q, d) order for ARIMA. And I can divide my data into train and test, and the ...
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Interpretation ARIMA dummy variables
I'm doing an ARIMA analisys with monthly dummy variables and other covariables. I'm wondering how to interpret the coefficients of my month dummy variables, as I don't have 12 months and I haven't an ...
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regression model with arma errors: forecasting the residuals
Suppose I estimate the following model:
$$
y_t = \beta_0 + \beta_1 x_t + \eta_t
$$
where $\eta_t$ is an AR(1) model, say. I can do that with forecast::Arima() as ...
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An ARMA model with white noise errors, that are ARCH? (How is that possible?)
First my assumption was that ARMA models take only the autocorrelation of the time series into consideration but not of the error terms (wrong!). But this assumption is wrong!
As the within ARMA ...
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How to handle recent variable change resulting in level-switch in time series modeling?
I developed a script to run time series models on people data, and I re-run the Arima model fitting/forecast reconciliation algorithm monthly as new data comes in. I use the grouped/hierarchical time ...
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Bad performance of ARIMA model on online buzz data, Any suggestions?
I was wondering if the ARIMA model is constrained to predict online buzz data (time series data).
What I want to do: Use the past round 30 months data to predict next month; and I use Python
Here are ...
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Include variance change at point in ARIMA model estimation in R
I have a series which I am trying to model through ARIMA approach. However, when checking the residuals, there appears to be a change in variance at a specific point. This is, given the residuals $e_t$...
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auto.arima (Hyndman R package)
I am running an auto arima on a datase that yields two tries as revealed by using trace=TRUE as:
...
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How to predict more than one future values in ARMA model
I want some help with predicting more than one future values in ARMA. I saw that the similar question has been asked here. But it is only helpful for predicting one future value.
For estimation of the ...
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How to intepret ACF and PACF plots? I'm trying SARIMA MODEL
I tried to put p=5 and q=7 but it's not working.
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ARIMA(0,0,3) fitted values and residuals in R
I am trying to understand the residuals returned from a fitted ARIMA(0,0,3) model. I simulated a time series and recovered the parameters:
...
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Using Box-Jenkins methodology for intervention analysis
I'm trying to follow B-J methodology for my intervention analysis. My understanding of of the first step is that, one should detrend any systematic trends such as seasonality then determine the lag ...
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Quantifying the impact of multiple time series on another time series
I have a few time series that correspond to the popularity of various documentaries about food, and other time series that correspond to outcomes of interest (various dimensions of food consumption). ...
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When do you know if you can discard data during the estimation of a model's order and its parameters?
I have been working with forecasting for a short while, and one thing has been clear so far: each problem is unique because data to each problem are unique. I find the variety of forecasting methods ...
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Maximum value of ACF at lag 1 for $\text{MA}(q)$ process
Given an $\text{MA}(1)$ process with parameter $\theta_1$, we know that
$$
\rho(1) = \frac{\theta_1}{1+\theta_1^2}
$$
which has a maximum value of 0.5 when $\theta_1 = 1$. I saw somewhere (I don't ...
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Calculate price forecasts from forecasted returns
I have a question which makes me so hurt.
Let's have a price time series $y_{t}$ for the same asset (for example, daily S&P 500 values) $y_{t}$
It can be trendy (trend stationary or difference ...
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Violated Residual Linearity/Homoscedasticity Assumptions in X13 Time Series Decomposition: How to Interpret and Resolve?
I have been working on decomposing a monthly time series of the country's exports using JDemetra+ with the X13-ARIMA-SEATS model. My aim is to break down the series into its Trend, Seasonal, and ...
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Assumptions in Changepoint analysis (EnvCpt) of Time Series
I'm looking to identify changepoints in my time series data. To begin, I've seasonally adjusted the data using JDemetra+. Next, I'll carry out a changepoint analysis utilizing the EnvCpt function in R....
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ARIMA(1, 1, 0) and explosive process
What is the difference between ARIMA(1, 1, 0) whose AR coefficient is smaller than 1 (e.g. 0.3) after being differenced and an explosive process whose AR coefficient is 1.3? Are these two identical? ...
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How to calculate forecasts with a SARFIMA Model?
I want to fit a multiplicative seasonal ARFIMA model of the form $(1 - \phi_1 L - \phi_2 L^2 - \ldots - \phi_p L^p) \times (1 - L)^d \times (1 - L^6) \times X_t = \varepsilon_t
$ and perform forecasts....
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Math specification with a Regression with SARIMA errors
Good day everyone, I have one question so I'm asking for your help.
I'm working on a model with a Regression with SARIMA errors, I already have the model and the forecast, but I need the ...
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arma confusion in R
Say I've got some AR(1) data from the model $x_t = .9 x_{t-1} + \epsilon_t$:
dta <- arima.sim(model = list(ar=.9), n = 1000)
I can instantiate an ...
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Testing Trend Stationarity against Stationarity
I am trying to find a test where null hypothesis is that the series is trend stationary. I can assume that the trend is linear if that is going to help. So the series for null hypothesis is given by:
$...
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How can I fit a SARFIMA model in python? [closed]
I would like to fit a model of the form $(1 - \phi_1 L - \phi_2 L^2 - \ldots - \phi_p L^p) \times (1 - L)^d \times (1 - L^s) \times y_t = \varepsilon_t
$ in python. $L$ is the lag parameter, $d$ is ...
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Is ARMA fit well-defined?
My question is whether unique time series has a unique set of ARMA parameters that fit it best, once order of AR and MA have been chosen.
For simplicity, I will ask only about ARMA(1,1) process. Lets ...
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