Questions tagged [forecasting]
Prediction of the future events. It is a special case of [prediction], in the context of [time-series].
3,258
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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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6 views
Time Series Forecast: Non-Stationarity of Exchange Rates and Fed Funds Rate
I am attempting to forecast the exchange rate $SGDUSD$ based on an autoregressive distributed lag model $ADL(p,r)$ as given by:
$$\Delta log(SGDUSD_t) = \beta_0 +\beta_1 \Delta log(SGDUSD_{t-1})+\...
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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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8 views
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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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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1answer
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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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1answer
29 views
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
8 views
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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9 views
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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13 views
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
23 views
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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9 views
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
10 views
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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16 views
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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20 views
Is this data cyclical? (ACF) [closed]
EDITED
I can see there is a trend, but not sure if the wave like is wavey enough to be cyclical?
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8 views
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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Predict Company Sales - Design choices; how to store incoming sales/which model to choose? [closed]
I am trying to predict the bookings of a stand-up comedian cafe. There are a lot of features I can use which have an effect on the number of sales. (e.g. day of the year, weather, average sales last ...
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1answer
13 views
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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1answer
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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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24 views
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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13 views
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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29 views
Is this MA(q) process invertible?
We have an MA $(q)$ model $Y_{t}=\epsilon_{t}-\theta_{1} \epsilon_{t-1}-\theta_{2} \epsilon_{t-2}-\ldots-\theta_{q} \epsilon_{t-q}$, whose parameters satisfying the following:
$$
\sum_{i=1}^{q} \...
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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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28 views
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
26 views
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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13 views
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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2answers
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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
20 views
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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17 views
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
22 views
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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1answer
29 views
Why strong seasonality in time series of total shipped volumes not observed in customer groups?
In a time series dataset, demand from various customers are given on a daily basis. When the data is aggregated at month level for all the customers together it is easy to see the effect of quarterly ...
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Times series forecasting , why predictions are the same over time
This is my first time posting here , I am doing an energy consumption forecast , my data contains the energy every hours I have two seasonality ,every 24 hours and every 7 days (daily and weekly).
I ...
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64 views
How to “add” coherent distributions from reconciled distributional forecasts?
I am trying to understand how to "add" coherent distributions from reconciled probabilistic forecasts, assuming the base forecasts are normally distributed as discussed in https://otexts.com/...
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1answer
35 views
When will a non-lagged regression term, in a forecasting algorithm, outperform an algorithm that doesn't require the regression term?
I am struggling to understand when a regression variable that is non-lagged would be beneficial to a forecasting algorithm.
I have been investigating the unobserved component model algorithm.
I am ...
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1answer
38 views
How to forecast for the next year given I have past 3 years data
I have a variable which is "% of a company's customers that participate in certain program" (that the company offers). Given that I have only data for past 3 years (I have month level data ...
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What are the characteristics of a trend and break stationary process?
I have a time series with around 380 data points (day-wise data acquired from instruments). I want to model these in ARIMA. It is my understanding that first I'll have to check for stationarity of the ...
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1answer
24 views
Predicting student enrollment based on school demand and birth rate
I want to know whether the decreasing number of students enrolled at an all-male school is due to decreasing birth rate, or to decreased demand for single-sex schools. I have the following data:
...
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How to forecast next week based on data of this week in R? [closed]
I have daily time series data. How can I forecast the next 7 days based on the data of the last 7 days in R? That means the value of this Monday should be the value of next Monday, the value of this ...
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1answer
14 views
Forecast accuracy of simulation vs empirical measurement
I want to compare how well a simulated curve approximates the "real" curve measured on empirical data.
More in detail: I have empirical data, let's say for simplicity the worldwide ...
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16 views
Modeling a collection of timeseries with censored data
UPDATED for clarity (originally I used the words "missing" and "censored" data interchangeably, whereas only "censored" is accurate in this case).
I am modeling a ...
