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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15 views

Real world inference problem for forecast probabilities

The set up: Me and a friend set up a website which employees of a small company (<10 people) submit forecasts for the next month's revenue of different sections of the business, and also an ...
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The neural network returns a straight line

I decided to write a neural network to calculate the evolutionary track. I have: MIST tables with evolutionary tracks Dataset converter that converts tables to ...
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time series forecast by extracting descriptive stats features

I am relatively new to time series data and I wanted to know whether forecasting a value into y(t+1) can be achieved by training a model using descriptive statistics (mean, standard deviation,max-min ...
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A state-space model for $\left\{Y_{t}\right\}$ when $\left\{\nabla \nabla_{12} Y_{t}\right\}$ is a stationary ARMA $(2,2)$ process?

It possible to find a state-space model for $\left\{Y_{t}\right\}$ when $\left\{\nabla \nabla_{12} Y_{t}\right\}$ is a stationary ARMA $(2,2)$ process? It seems to be extremely difficult for me to ...
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Discussion model selection. Predictive value for daily sales forecast

I am trying to predict the number of sales for a given day for a stand-up comedy cafe. Aside from a variety of predictive values (day of the week, average sales last 30 & 60 days, day of the year ...
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Counterintuitive results modeling ARIMA in differences

I have developed an ARIMA model in differences (externally differentiated, not in the Arima() function of the forecast package) that uses the Unemployment Rate variable as an external regressor. The ...
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Fill missing values when aggregating a time series from minutes to hours

I am attempting to predict future values using AWS Forecast, and my data is by minute. However, due to data size constraints, I need to aggregate this data to hourly. The problem is that I am missing ...
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Do standard errors from a growth-rate forecast get added up cumulatively?

I have an aggregate macroeconomic time series variable, which one would expect to grow exponentially. So, in order to forecast it, I use the stationary time series of its growth rate over time. I am ...
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How to use Machine learning to outperform time series

I am building a ML model to predict future TV audiences based on historical audience data. We currently have a forecasting tool that is using timeseries modelling to make its predictions (mainly ARIMA ...
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Good books on autoregressive distributed lags (ARDL)

I would like to find a book on ARDl not part of a general time series book, but focused on ARDL primarily (I have read books on time series and articles on ARDL already - I want an extensive treatment ...
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Selecting exogenous factors lag to develop an ARIMA model for daily forecasts

We are trying to assess the possible effect of some meteorological variables on cumulatives reported cases of the actual covid-19 disease for a wide range of countries at the daily timestep. Then, ...
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Forecasting Sales using Prophet in R

Could someone please help me figure out how to adjust my code so that it gives better predictions? Right now they are way to high. I'm wondering if it is something to do with the holiday lag. I'm not ...
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Forecast Fit always one step forward

I'm making a projection using time series, but I have a problem that I can't solve. My forecast fit is always one step (month) ahead of my data. What do I mean by this? The movement of the curve of my ...
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What does a straight line of residuals of a time series mean?

I used Python's statsmodels' seasonal_decompose & received the following plots: I understand that the residuals here are what is leftover after trend & ...
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How do I identify if my time series' seasonality is additive or multiplicative?

The "ExponentialSmoothing" function in Python's "statsmodels.tsa.holtwinters" library gives you the option to set trend equal to ...
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Multivariate, multi-series time data more appropriate for regression or ARIMA/time-series?

I have the following case for which I am needing to forecast a value, say, 12 months out: Many individual entities each with their own time series. Each entity has the same data structure For each ...
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Can you even forecast/project something like employment diversity? [closed]

Can you even forecast/project something like employment diversity at the workplace? For example: gender, visible minorities, etc. How would you go about doing that? Thanks in advance
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What model should I use to forecast retirement in HR data? [closed]

What type of forecasting model is most suitable to look at HR data trends such as Retirement forecasts? Any suggestions is appreciated!? Thanks so much
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1answer
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What is the correct way to evaluate time series forecasts with seasonality?

I am working on an energy demand forecasting project, where I am using the Facebook Prophet model. I have used 3 years (2017-2020) of training data to forecast energy demand for a week at an hourly ...
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time series forecast questions: train / test and data split

I'm newbie to forecast and working on a time series. I begin with splitting data (lets assume there are 100 daily observations) into train set (first 70 days) and test set(last 30 days), then fit Holt ...
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If extrapolation is bad then how is forecasting methods statistically relevant

There are lot of articles out there that talks about why extrapolation is a bad thing to do. My question is if the above is true , how are forecasting methods like forecasting the trend based on some ...
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forecasting sales using dummy variables for seasonality by week and holiday lags in excel

Hi I'm trying to create a forecast in excel that uses dummy variables for the weeks to create seasonality as well as dummy variables for certain holidays. I created 52 weeks and then had them be 1 if ...
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1answer
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Did I calculate the non-seasonal Mean Absolute Scaled Error (MASE) correctly?

This is the formula: Here is the link to page in the book. I am not confident I am interpreting the formula correctly. Below is my data: I calculate the non-seasonal MASE to be 1.125. Is this ...
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Forecast running challenge completion date in excel

I want to add in a cell that shows the predicted completion date of a running challenge I am doing, based on my performance so far. At the moment I have a cell that works this out on a very basic ...
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Should I make the problem “easier” or “harder” for predictive maintenance by changing the available data?

Consider the standard method of labeling discrete windows "normal, 0" and "failure soon, 1": If in the second case I want to predict further ahead of time, I simply push the 1s ...
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auto.arima question

I predict monthly spending data two years ahead of time using a variety of models. One of the models I am experimenting with is an arima model chosen through auto.arima. The problem the last time I ...
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1answer
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auto.arima - What am I doing wrong?

I am using auto.arima to do a forecast of a series but I am getting really poor results. I think that I am doing something wrong when writing my code. I use 120 observations as my training data to ...
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Azure AutoML: problem with univariate time series forecasting [closed]

I'm having troubles generating univariate time series forecasts with Azure Automated Machine Learning (I know...). What I'm doing So I have about 5 years worth of monthly observations in a dataframe ...
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PACF for ARMAX Model

How do I can get the PACF graph for one dependent variable, Y and 2 independent variables? do I have to calculate the covariance? the PACF output shows in R is only 2 variables only. There is a ...
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1answer
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Univariate time-series data with 30 minutes interval - Which algorithm to use for forecasting?

I am working on a case study to predict # of calls for next 30 days. The data is at 30 minutes interval and start from 7 am to 23:30 PM (So 34 observations in a day). I have two questions: What would ...
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Demand Forecasting with Price Promotions

I'm trying to use Excel Solver and Linear Regression to forecast demand of a product with variables like different types of promotions and baseline level. I am looking at pooled regression but I don't ...
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identify tentative arima model from acf and pacf graphs

here are the ACF % PACF graphs for three different models, kindly please tell teh tentative ARIMA models using these graphs, I will be very much thankful to you
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Regression with SARIMA errors

After an SARIMA transformation, is $\epsilon_t$ equal to the difference in observed original $y_t$ from its estimate or the equivalent quantity for transformed $y_t$, $y'_t$? The motivation: I am ...
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How to combine time series and factor data to build a model for forecasting in python?

I am working with electrical load data. I have a time-series data of load over time and some factor data (eg, day type, temperature, humidity, airspeed, etc). I am willing to build a machine learning ...
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1answer
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How to use the AIC/BIC for overfitting (information criteria) ARIMA

So I am using STATA, I have the log likelihood, AIC and BIC as such: AIC: -112.1838 BIC: -100.2412 log likelihood: 64.23 N= 200 observations So how do I conclude that there is no "over fitting&...
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log transform a time series before fitting an ARIMA model?

That is my line graph using my data above in the snipboard link ^ Do I need to log-transform my data before doing ARIMA? I cannot see any variance increase in my opinion.
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ARIMA (p and q values)

I am trying to do Arima forecasting, i differenced once so d=1, Im not sure what my p and q values need to be, please check screenshots of acf and pacf below:
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How do I treat zero inflated univariate time series data?

The data I am handling is a rainfall data. The only columns are "Date" and "Rainfall". The day that is not raining will be accounted to zero, therefore it is a zero inflated ...
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Forecasting based on previous data values and repeating that each week in R

I have this file: a.csv For example on a.csv, for store 1000 and store 1001, there are values for all 7 days of the week for 3rdparty, dinein, drivethru, digital, and takeout: store day of week ...
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ETS (M,M,N) model Distribution [closed]

I am using ETS (M,M,N) model, where: Yt = Lt-1 * Tt-1(1 + ε_t) Lt = Lt-1 * Tt-1 (1 +αε_t) Tt = Tt-1(1+βε_t) How can I check that yt+1 follows a normal distribution ...
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How can I determine that a forecast is significantly more accurate than another one? (time series)

Reproducible Example Look at this reproducible example: I have a time series that I want to forecast. For the sake of reproducibility, I'll just take AirPassanger. ...
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Forecasting with lagged data

I'm trying to forecast some values with information about many variables from the past, there's a description of the data: It contains information over the time from 5 sensors for different variables ...
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1answer
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Non stationarity and forecasting

Let's assume we have estimated a linear regression model on a dataset from 2000 to 2017. The data were stationary. What happens if the data are no longer stationary in the next years? Do the forecasts ...
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What is the equation of ARIMA(2,1,3)(1,1,1)?

I have constructed an ARIMA model in R. I would like to know the mathematical equation of the model, I am stuck when it comes to the seasonal part. here it is: ARIMA(2,1,3)(1,1,1)[12] Coefficients: <...
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Forecasting $X_{t+2}$ for causal AR(p)

Let $X_t$ be a causal $AR(p)$ process. Compute a linear forecast $X_{t+2}$ based on $X_1, X_2, ..., X_t$ for $t \geq p+1$. If $AR(p)$ is causal it means that it can be rewritten as a linear process: $...
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1answer
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ARIMA for intervention time series?

I am completely new to R, time series and ARIMA, so bear with me. I have a time series data from 2018 to 2020 that shows an intervention in early 2020. When plotted, the graph quite dramatic plummets ...
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Getting equation von SPSS Forecasting Output (ARIMA)

I am having trouble deciphering the equation from SPSS Output for a time series model. In particular this concerns two outpust. The expert model suggest for a time series of monthly sales a MA 1 ...
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How to interpret regression results when the data have been detrended?

I am planning to build a linear regression model where I explain flight ticket demand with airfares, lagged airfares, GDP etc. based on monthly data from the past 15 years. This is my first time ...
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R Time series - negative predictions- why and how to solve?

I am trying to do a timeseries forecast using R packages. The data I receive is general and the current approach is to try Arima, ETS, STL and pick the mode with the least MAPE. This works for most of ...
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1answer
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Forecasting probability distribution for year-ahead resolution

Background I am currently working on a problem to study the dynamics of aggregate losses in some state-owned companies in my country. I was successful in gathering data of losses across 34 such ...

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