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Questions tagged [forecasting]

Prediction of the future events. It is a special case of [prediction], in the context of [time-series].

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Minimum sample size for time series cross-validation (tsCV)

I am doing cross-validation of an autoregressive neural network model and I am using the tsCV function (forecast package) ...
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What are the confidene intervals when plotting the forecast function in R?

So I have forecasted with the 'forecast' function in Rob Hyndman's "Forecast" package using R. But when plotting it I am in doubt what the blue and grey area in the graph means. I assume that the blue ...
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Predict song genre using LSTM

I have a dataset of songs based on genres. For example, a song may hold {5, 2, 3} as scores set for Sentimental, Rock and Jazz. In total there are 800 songs sequentially arranged. I want to predict ...
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1answer
23 views

What is the meaning of an autoregressive parameter greater than one? [duplicate]

I have created a AR(2,1,0) model with the first two parameters equal to -1.08 and -0.33. I understand that a autoregressive parameter equal to 1 implies non-stationarity and a random walk process so I'...
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What is the equaton for ARIMAX(1,1,1) and how can I undifference the 1st differenced data to fit the equation? [duplicate]

I have generated the ARIMAX(1,1,1) model to predict the future Barramundi catch. In this model, there are two exogenous variables (price and streamflow) that affect Barramundi catch. I have used 1st ...
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Pricing transfer prices for oil hub? 390 Days of prices given

Need some input in how to attack this problem. Given are 8 timeseries: UK Oil price, Delivery Quarter 1 2020 UK Oil price, Delivery Quarter 2 2020 UK Oil price, Delivery Quarter 3 2020 UK Oil price, ...
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2answers
33 views

Differencing and trend in time series forecasting

I understand that a time series is differenced to remove trend. But if trend can be modeled for forecasting purposes then why difference a time series at all?
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1answer
136 views

What is the difference between an accuracy measure and an error metric?

The two concepts are distinct in measure theory. Nonetheless, moving out from measure theory, the two terms are often used interchangeably. To most forecasters, especially forecast practitioners, they ...
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22 views

How to do location forecasting on Chicago Crime Dataset?

I am using the dataset https://www.kaggle.com/currie32/crimes-in-chicago and given primary type of the crime I want to forecast the next location of crime. What approach should I follow ?
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How do you forecast ARIMA with multiple regressors? [migrated]

The complete R data and code for my question is here: https://pastebin.com/QtG6A7ZX. I am new to R and still a beginner when it comes to time series analysis, so please forgive my ignorance. I am ...
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How to interpret ARIMA(0,1,1)(1,0,0)[12] with drift from R? [duplicate]

The code that Î used to generate ARIMA summary is, arimafore = forecast(auto.arima(sales), h = 24) summary(arimafore) and i got this output Forecast method: ...
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Recurrence of $k$-step ahead forecast with ARMA

For brevity, let's consider an AR(1) model, but this question should apply to ARMA(p, q) in general. Assume we are at time $T$ and would like to forecast $k$ steps ahead, $$ X_{T+k} = \phi_0 + \phi_1 ...
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1answer
48 views

Make daily business data stationary for ARIMA

For my master thesis I have a dataset with the daily count of orders from a company over ten years. Naturally this data follows strong seasonality with almost no orders on the weekend. To fit an ARIMA ...
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Is stationarity a requirement when using neural networks for time series forecasting?

I'm getting conflicting information on whether stationarity is a requirement when using neural networks for time series forecasting: In this lecture, the speaker says it isn't a requirement. In this ...
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What is the best model to forecast ACT scores using practice test scores and past student data?

I understand that I may not be asking this question correctly, and would appreciate any feedback possible in order to help set me on the path to figuring this out... I work at a high school where ...
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1answer
234 views

Model for spatiotemporal and discrete variables

I have a situation where I am monitoring events at 50 or so geographical sites in a town and at each of these sites, I am making measurements regarding the count of certain particles (so the ...
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ARIMA predictors - clarification

I'm working on multivariate time series (still), and would like some clarification. I was reading this site: Duke Forecasting and I came across this statement: "We see that the most significant ...
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1answer
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acf and pacf suggests MA but auto.arima gave AR

I have this data which is residual series obtained from predicted values and observations. original series was a random walk with a very small drift(mean=0.0025). ...
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1answer
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Forecast existed ARIMA model using primer time-series

I have some fitted ARIMA model: ...
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20 views

How to properly add spatial features for a precipitation time series forecasting?

I am reading this paper. The center of the circle is the site where the model should forecast precipitation. Red stars in the picture are nearby sites and each site has these features: I want to ...
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1answer
48 views

Getting best fitted model using Auto ARIMA but prediction result is very bad

I saw this: time series - Poor prediction using ARIMA model But the answers aren't clear and isn't directing to me for solving the problem I have. Using only AR is giving me better prediction whereas ...
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Forecast efficiency: why no correlation between errors and available information?

(Applied Economic Forecasting using Time Series methods; Ghysels, Marcellino, 2018), in the chapter about forecast evaluation, relates efficiency as "the efficient use of the available information". ...
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Kalman filter on stock sentiment time series

I was wondering if & how I can use a Kalman filter on my dataset which contains closing prices of stocks + sentiment scores of tweets about that stock for each day in a timeframe of 1 month. e.g....
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How to choose the right forecast method for variable 'X' when I have some available forecast for variable 'Y' with historical data of X and Y?

I have yearly historical data for variables 'X' and 'Y'. Say the time frame is 't'. In addition to available historical data, I also have the forecast data of variable 'Y' for t+1. My aim is to ...
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3answers
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Can my data be white noise if the mean >0?

According to the auto-correlation method, my time-series is white noise (i.e. 95% of ACF within ±2/√T), yet the data are counts and thus the mean >0. Are these two facts incompatible? I'm using the ...
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How to calculate forecast given variation by day

I'm trying to work through a problem and I'm wondering if I'm interpreting it correctly. Let's say we predict the price of stock (today worth $50) to vary by N~(0,1) every day, and you are looking to ...
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How does forecast skill score change when seasonality in the forecast quantity is removed?

Given RMSE skill score $s$: \begin{equation}\label{eq:msess} s = 1-\frac{\text{RMSE}(f,x)}{\text{RMSE}(r,x)}, \end{equation} where $f$, $r$, and $x$ are forecasts of interest, reference forecasts, ...
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2answers
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Fitting a GARCH model and forecast using validation set approach In R

I have seperated the data into training and testing data. Then I fitted this simple garch model for training data as follows,(using rugarch package) ...
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2answers
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Time Series: Confused about identification of (possibly?) an ARMA(p,q) model

this is my first ever question on a website i use frequently! This time series has given me much trouble over the last couple of days even after extensive googling, I suppose with TS theres no two ...
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1answer
34 views

What is the difference between probabilistic forecasting and quantile forecasting?

A probabilistic time series forecast outputs the entire distribution of the forecasted values for a given time point, instead of just a mean or a point forecast. A quantile forecast is a forecast ...
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26 views

Feature engineering suggestion

I've to forecast the revenue generated by a company on a monthly basis. The dataset looks like this: ...
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1answer
22 views

Dummy/baseline models for time series forecasting

I am working on an evaluation of time series forecasting models in Python, more specifically with statsmodels, scikit-learn and tensorflow. I think it makes sense to first compare the model ...
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1answer
34 views

Need help with lag features in regression forecasting

I am trying to build a timeseries prediction model. The problem is that I'm still hesitant whether I should use lag features or not. What makes me wonder is the fact that the training data has these '...
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Forecasting a year ahead using annual vs daily data

Suppose, as an example, that you would like to forecast a share price in a year's time based on the past 20 years of data. You can either use annual data and forecast 1 period ahead, or use daily data ...
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Forecasting using MA(2) model when past 5 observations are known

So given an MA(2) model : Xt = Wt + Theta1 * Wt-1 + Theta2 * Wt-2 Where Wt is white noise. (Normally distributed) and Theta1 and theta2 were available. Say if X96,X97,...X100 of the series were given ...
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2answers
425 views

Why is arima in R one time step off?

I've recently noticed an odd behavior in a few timeseries methods. Let's fit an arima model (ar1) to the annual subspots data ...
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0answers
30 views

Elastic net chooses lags beyond ACF cutoff

I've been using Elastic net for time series forecasting. I’m using first difference of the series. Normally I use the ACF to determine the number of lags to use. I was curious, if I would produce more ...
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How to handle partial observations of the variable of interest when training a time series model?

I have the following time series data: $\{ t_i, X_i, Y_i \}$ where $i$ is the index, $t_i$ is the timestamp, $X_i$ the measured value of the external variable and $Y_{i}$ the value of the variable ...
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1answer
32 views

Linear Forecasting with a small dataset

I am trying to get some forecast (5 years more) from a small dataset that is as follows: ...
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11 views

Algorithm for producing a Moving Average (as in ARIMA) model

I have a time series $X_t$ and I want to produce an ARMA forecast (without using any automated packages - the purpose of my project is to understand how those work). So far, I have the AR(p) part ...
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14 views

Forecasting with time series with different interval

I am trying to forecast monthly inflation rates using weekly percentage change of commodities prices. Is there a way to do this without losing info? or can I like get the predictor's moving average ...
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1answer
55 views

auto.arima throws error wrong length for 'fixed' [closed]

I am using auto.arima from the R forecast package. When using this function with lambda parameter, it is throwing error wrong length for 'fixed' Here is the code ...
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1answer
126 views

Forecasting daily data with annual seasonality

i have been trying to do the forecasting model. My data has daily value and there is annual seasonality and probably weekly. My question is which model will be the best. I have tried with SARiMA but i ...
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1answer
39 views

Forecasting with AR(1) and pseudo out-of-sample using R

I'm trying to do Pseudo out-of-sample forecasting using R. And, I also have the following initial data (gdp) ...
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3answers
314 views

Does lack of seasonality imply random time series?

Some techniques for time series analysis (prediction) require that the time series not have seasonality. It seems that without seasonality, a time series is essentially random, in which case ...
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1answer
42 views

K in Fourier series - How to find value of K to use it in ARIMA?

I am using the forecast library for doing some time series forecasting. I need to forecast number of sold items. I am planning to add holidays as xreg in auto.arima. The holiday will be a 0/1 list, ...
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19 views

How to get a forecast equation for $\hat{y}$ using ETS state space model

The ets(AAA) state space model (Rob Hyndman's handbook) is as below State equation is \begin{equation} Y_t = L_{t-1} + b_{t-1} + S_{t - m} + \varepsilon_t \end{equation} The measurement equations ...
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0answers
20 views

Dynamic regression with lagged explanatory variables

I have data on unemployment from 2006 to 2018(monthly) and have fitted a $sARIMA(3,1,1)(0,1,1)_{12}$ that has decent forecasting abilities, however I want to try to improve the forecasting abilities. ...
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14 views

Timeseries forecasting repeadted results

I'm trying to fit GBR on a timeseries of profit of a company. The code looks like this: ...