Questions tagged [forecasting]
Prediction of the future events. It is a special case of [prediction], in the context of [time-series].
1,384
questions with no upvoted or accepted answers
3
votes
0
answers
290
views
Can Intervention analysis be used to forecast time series
if I have an estimate of the intervention variable from a similarly interrupted time series can it be used to forecast another similar time series after the effect of intervention.
For example lets ...
- 61
3
votes
0
answers
158
views
Seasonal ARIMA Forecast
I'm studying ARIMA at the moment with application to seasonal data sets. R lets you forecast using selected models but I'm just wondering what formula is used to compute these forecasts. For example, ...
- 73
3
votes
0
answers
2k
views
How can i do time series forecasting with missing data
I am relatively new to time series forecasting, I have worked previously with continuous data at regular intervals successfully, Now I have a data set with missing values,
for example look at the ...
- 141
3
votes
0
answers
939
views
Forecastability and Coefficient of Variation
I'm trying to get a sense check here. When determining "forecastability" for sales data, I tend to use the CV. However, this is highly susceptible to seasonality and outliers. As such, I was wondering:...
- 31
3
votes
0
answers
288
views
Why only full ARIMA models in auto.arima?
It seems that the auto.arima function in the "forecast" package in R only considers full ARIMA models. By "full" I mean that if an AR lag $k$ is included, AR lag $j$...
- 55.5k
3
votes
0
answers
3k
views
Stock closing price forecasting using ARIMA model in R
I have downloaded the daily stock Adjusted Close price of one stock from sep 2011 to till date. As per my study plan, I have plotted some basic plots to understand the daily stock Adjusted closing ...
- 1,529
3
votes
0
answers
1k
views
Panel data forecasting from Arellano-Bond GMM estimation
I want to come up with predictions of final energy demand per capita (fe) for a panel of countries. Explanatory variables are GDP per capita (gdp) and population density (pop) -- all variables are ...
- 183
3
votes
0
answers
229
views
Verification of assumptions in TBATS model
I have a question about using BATS/TBATS models implemented in the forecast package for R. In De Livera, Hyndman & Snyder (2011) the models are used without any following analysis. Is it OK to ...
- 31
3
votes
0
answers
163
views
Sales forecasting to account for regression
I have a very beginner question. I am attempting to forecast total 2014 unit sales of a large number of products.
The data I have has 10 points for each individual product, which are the total unit ...
- 183
3
votes
0
answers
260
views
Why is the Chow test with $R^2$ wrong?
The regular way to compute the F-value for a Chow forecast test is:
$$F=\frac{(e_R'e_R-e_1'e_1)/g}{e_1'e_1/(n-k)}$$
My professor said something today about that a Chow forecast test using $R^2$ would ...
- 31
3
votes
0
answers
145
views
Should I de-mean a predictor variable before a dummy interaction
Suppose I have the following time-series linear model where $\beta$ is misspecified:
$Y(t+1) = \alpha + \beta X(t) + \sum_{i=1}^{10000}\gamma_i Z_i(T) + \varepsilon$
where all parameters are in $\...
- 671
3
votes
0
answers
719
views
How to use error term in AR (2) model for predicting future values?
We use turbidity to estimate suspended-sediment concentration (SSC)- our data was serially correlated. We ran an ARMA process and ended up with a AR (2) model. Our equation in log form is:
estlogi(...
- 39
3
votes
0
answers
253
views
How do I forecast a timeseries of data using GARCH(1,1)?
I'm new to GARCH, but I've got daily data of TV Ratings. I've been trying to forecast this for future, and a quick background - the data is non-stationary, has high seasonality (weekly, monthly & ...
- 51
3
votes
0
answers
2k
views
R forecast package: How to combine fourier terms with another XREG matrix
I'm using R forecast package with a daily time series data, that has complex i.e. Multiple seasonality (weekly, Yearly, monthly). The fit/forecast process also needs to take into account certain day ...
- 181
3
votes
0
answers
135
views
Prediction model problem
I am trying to design a model that can estimate the number of customers I will receive in every store every month using the number of customers I received every month in every store for the last five ...
- 149
3
votes
0
answers
178
views
Calculation of seasonal (annual) component of time-series: use of cross-validation?
I've been working for almost a year on electricity load forecasting in collaboration with some climate scientists, using temperature data obtained from models. Instead of using directly temperature ...
- 1,016
3
votes
0
answers
175
views
Modeling relative contribution of a variable
I am overthinking this for sure, but I am stumped. I have a historical data set of projects with hours of contribution by various positions. There are six types of projects. How can I model the ...
- 146
3
votes
0
answers
68
views
Updating a set of estimated forecasts
Suppose I have some stochastic process $X_t$. At each time $t$, I receive an estimated probability distribution for $x_t$, followed by an observation $x_t$. After receiving a set of observations ${x_1,...
- 9,200
3
votes
1
answer
188
views
Forecasting with after x lags values
I like to build a forecasting model where am allowed to use only l lagged values.
That means the model should forecast only l lagged values like $y_{t}$ can be only predicted using values $y_{t-l}$, $...
2
votes
0
answers
19
views
When do ARMA models fail?
I have just started learning about Autoregressive–moving-average model (ARMA). On the Wiki page, it has been mentioned that:
ARMA is appropriate when a system is a function of a series of
unobserved ...
- 211
2
votes
0
answers
15
views
Statistical test for temporal cross validation
I estimated the performance of my forecasting model and that of a baseline on 10 folds using temporal cross-validation. With which test do I assess if my model is significantly better than the ...
- 423
2
votes
0
answers
31
views
How to use low-frequency data when forecasting high-frequency time series?
Good afternoon!
Consider a problem: a panel regression is fitted to predict corporate bond yield credit spreads. Under "corporate bond spreads" term I mean difference between yield to ...
- 93
2
votes
0
answers
24
views
Does one network predict the other?
Here is my problem:
I have two undirected networks, $G1$ and $G2$ which change over time
The nodes in each network are identical
The edges are always constrained between 0 and 1
I want to know ...
- 219
2
votes
0
answers
63
views
Lower RMSE but worse model prediction
I am using a KNN model to predict quantity sold for a highly seasonal business. I chose KNN because I thought that using nearest neighbors would inform my model about said seasonality better than a ...
- 21
2
votes
0
answers
42
views
Time series: how much past predicts future
In financial (time series) statistics and forecasting we usually assume that the past of a series can predict the future to some extent. Every financial ad will warn you that investors should not ...
- 263
2
votes
0
answers
21
views
Forecasting by autoregressions of invertible linear transform of variables
Suppose I have two variables and two alternative variables, where the latter are an invertible linear transform of the former. Suppose further, that I estimate both the new and the original variables ...
- 2,587
2
votes
0
answers
111
views
Is ARIMA the wrong approach or am I missing something?
I am predicting a time series by splitting the prediction model in a deterministic linear regressive part and a stochastic part. The data was split into a training set of 2190 timesteps and a test set ...
2
votes
0
answers
157
views
What is the name for a simulated 'now' date in the past?
In time series analysis we often set up a test where we forecast as if we are a certain date in the past. Is there name for such a date?
Example:
Monthly sales data from January 2016 till July 2021. ...
- 113
2
votes
0
answers
48
views
How to transform a daily average temperatures forecast into an hourly forecast based on the hourly temperature profile observed historically?
I need to transform a daily average temperatures forecast into an hourly forecast based on the hourly temperature profile observed historically.
I work in Python.
I have found ways of forecasting the ...
- 21
2
votes
0
answers
261
views
What is the best approach to grouped time-series forecasting?
Let's say we have data on the number of clicks per user over quite a long period of time. We can use, say Facebook Prophet, to forecast daily values given that we have enough historical data. That ...
- 106
2
votes
0
answers
99
views
Forecasting: AIC, AICc and BIC VS Cross Validation for model selection (cases for different horizons)
The majority of the automatic model selection algorithms like auto.arima and ets (https://robjhyndman.com/publications/automatic-...
- 21
2
votes
1
answer
29
views
What is this measure of error called?
I am investigating prediction errors in a context where the errors can be extremely large. Someone advised me that in addition to reporting the mean or median of the absolute errors $$|\hat{x} - x|$$ ...
- 683
2
votes
1
answer
146
views
Which tree ensemble algorithms are the most suitable for time series forecasting (regression)?
Decision tree ensemble models are very practical for building predictive ML models. They are not strict on assumptions, can work on data without too much preprocessing, train fast and typically result ...
- 381
2
votes
0
answers
23
views
Is there anyway I can limit my forecast to a upper limit using bsts package?
I am using bsts package for forecasting. I need my forecast to be less than the upper limit. Like shown in the below figure.
FaceBook Prophet allows this by an option. I would like to do the similar ...
- 21
2
votes
0
answers
43
views
Model generated 2 different results, which is the best SARIMA model?
I got 2 different forecasted results using different orders using SARIMA model. I am unable to choose the best model out of the two below. One have very low AIC but the SR1 co-efficient is close to 1 ...
- 141
2
votes
1
answer
78
views
Time Series & Stationarity
I know that Seasonality and Trend violate the principle of stationarity, so before modelling the time series with many statistical models like AR, MA and ARMA it's important to remove those components ...
2
votes
0
answers
201
views
Large dataset of many short time series: what model to use for forecasting a new time series not in the data?
Problem statement
Consider this hypothetical but hopefully practical example:
You have a dataset consisting of home electricity usage for 1,000 homes in a city.
For each home, you have a time series ...
- 11.5k
2
votes
0
answers
75
views
Should I always convert time-series data to stationary before forcasting?
I am trying to predict how much revenue a store will generate in next month based on revenues of previous months. I was doing simple regression for forecasting before, but I have recently read about ...
- 133
2
votes
0
answers
372
views
What are the underlying statistical differences between the vector autoregression model and Prophet?
I am trying to understand the underlying fundamental/statistical differences between vector autoregression models and Facebook's Prophet, with regards to multivariate time series forecasting.
I am ...
- 33
2
votes
0
answers
31
views
Application of xreg in forecasts
I'm trying to compare my forecast values with real values and assess the difference. I have 130 observations which are number of deaths (dependent variable). As well I have data for interaction ...
- 247
2
votes
0
answers
287
views
Forecasting with extrenal regressors in R using RUGARCH
I am struggling to find the solution of my problem, I want to model the volatility of the DAX index using some explanatory variables to do so. I am using the rugarch packed and I model the series has ...
- 21
2
votes
0
answers
205
views
ARMA: order selection with LASSO
I'm trying to forecast daily data (I have 15 years of historical data) with complex seasonality: weekly, monthly, annual and also irregular seasonality due to moving events like Easter.
As suggested ...
- 21
2
votes
1
answer
66
views
Time series - Differencing vs Dividing
What is the difference between using differencing or dividing to treat trends / seasonality ?
Most approaches seem to be using differencing. Is there a qualitative preference for why we do this ?
For ...
- 21
2
votes
0
answers
497
views
Regression: Causation vs Prediction vs Description
In my experience it seems me that the interpretation about regression, its meaning and its scope, are debatable and great confusion exist about those things. It seems me that confusions are not go ...
- 4,064
2
votes
0
answers
18
views
Calibration and consistency for predictions over time
It's common to plot the predicted probability of some event/events versus the actual fraction of such events that happened to obtain a calibration curve showing whether the predictions were under or ...
- 23k
2
votes
0
answers
52
views
GARCH Model Forecasting to Incorrect Time
I have the following garch code, I am modeling 10 years of data for the SP500
...
- 21
2
votes
0
answers
18
views
What is an appropriate statistical technique to use with rates of decay/growth for this estimation problem?
I have the following problem:
I have ground truth data on the population growth and the population decay of a certain breed of rabbits (let's say $R$) from $T_1$ to $T_n$.
Now, I want to estimate ...
- 215
2
votes
0
answers
157
views
Regarding Hyndman's approach to estimating prediction intervals for forecasts generated by neural networks
I'm currently looking for ways to estimate prediction intervals from an LSTM generated forecast. Several advanced methods are suggested in the literature (e.g. SQF-RNN), but as a first pass, I'm ...
- 10.7k
2
votes
0
answers
33
views
Predict Future Sales
Which modelling strategy (time frame, features, modelling technique) would you recommend to forecast 3-month sales for total customer base?
At my company, we often analyse the effect of e.g. ...
- 21
2
votes
1
answer
222
views
Scaling the MAE by the mean of non zero points for intermittent data
I am currently trying to find a way of scaling the MAE for my intermittent data.
The data is always greater than 0 and is intermittent, with long periods of zeros.
I have read a few papers that ...
- 21