Questions tagged [forecasting]

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

Filter by
Sorted by
Tagged with
3
votes
0answers
18 views

Seasonal ARIMA- non stationarity after differencing and seasonal differencing

I am working with a seasonal time series, which is initially stationary. After many attempts, the best model that fits the data is an ARIMA(0,1,4)(0,1,1)[12]. However, checking for the stationarity of ...
1
vote
1answer
324 views

How does facebook prophet handle missing data?

The Prophet paper (forecasting at scale by SJ Taylor - 2017) says the following on missing data: Unlike ARIMA models, the measurements do not need to be regularly spaced, and we do not need to ...
0
votes
0answers
15 views

Forecasting time point not value

I have a simple question. when we want to forecast a time series, we always focus on the value of series in future. But could we forecast time point of spesific value? For example I would like to ...
0
votes
1answer
44 views

How can I quantify the impact of the lag between two events that each occurred twice?

There's a question about the impact of holding sales close to one another. Last year, Sale A was held four weeks before Sale B. This year, Sale A was held three weeks before Sale B. How would I go ...
4
votes
3answers
266 views

Applying an interaction term to all the IVs

I have a linear model with 6 IVs and would like to analyze the effect of an interaction term applied to all the IVs. To illustrate, let's say we're predicting the Win/Loose ratio of NBA basketball ...
0
votes
0answers
294 views

What is the best model for forecasting if you have very less data points? [duplicate]

I recently got to work on a problem of forecasting five years of data. But I only had five data point from previous years i.e. yearly data (frequency = 1). The data is heteroskedastic. For example, [...
0
votes
0answers
18 views

Predictive model fusion informed via observed values

Suppose I have some quantity I want to forecast, like the traffic at a particular intersection or the sales volume at a particular store. I have three sources of data to make use of: Broad-scale ...
0
votes
0answers
17 views

How to Encode Dummy Variables into a Neural Network

I am currently creating a neural network(LSTM) for electrical demand forecasting and I want to include dummy variables to tell the model to treat weekdays differently from weekends, treat working ...
0
votes
0answers
17 views

dimensionality reduction using SVD for forecasting with machine learning

I'm using a LSTM model to forecast time series data. My dataset has far too many variables and I would like to perform dimensionality reduction. My LSTM model works on a rolling window of 500. I ...
2
votes
1answer
76 views

How to predict weekly or monthly sales from daily time series model?

I've been given daily data and I've trained a SARIMAX time series model in Python so that I can predict daily data if given daily input. However, I need to forecast on a monthly or weekly level, ...
0
votes
0answers
6 views

Single Variate Fixed Period Lagged Regression

I found a relationship that seems strong, but I'm not finding corroboration of it in research papers, so, am I missing something obvious? I have data (for simplicity of explanation) ...
1
vote
1answer
31 views

Training ARIMA based on overlapping hourly weather forecasts

I am working with hourly water level data and I plan to forecast each day the next two weeks (on an hourly base meaning 14*24 = 336 forecasts each day). My regressor is an hourly weather forecast. ...
0
votes
1answer
231 views

How to align two seasonal time series

I am trying to decompose a time series using Holt Winters method and use it for forecast. I am trying to do this for weekly data of last 25-26 months. The challenge is that the dates of the seasonal ...
1
vote
1answer
224 views

Models that train on Mean Absolute Error or similar?

I'm trying to do time series prediction and I'm interested in training on MAE or other custom loss functions. For my problem I'd prefer having errors of {0, 10, 0, 10, 0, 10} as supposed to {5, 5, 5, ...
4
votes
3answers
101 views

Is it a valid claim, that by differencing a time series, it loses its memory, and as a result its predictive power?

Marcos Lopez de Prado seems to be a well known and renowned machine learning expert in the field of finance. I am very far from his level, as have not yet finished my PhD in economics, and only have ...
2
votes
1answer
48 views

How to interpret straight line as forecasting

I would like to make some short term forecasting using an AR(I)MA model. having the following daily time series, which is for the raw data: It seems to be like a white noise, based on the acf and ...
0
votes
2answers
61 views

Forecast sales and then ungroupto find individual sales

I am trying to solve a problem for a brewery: A brewery has 50 beer types in total out of which only 8 to 10 beers are available on tap for a single day i.e only 8 to 10 beers will be sold on any ...
4
votes
1answer
48 views

Do out-of-sample fitting methods solve the problem of over-fitting?

Suppose we have a regression model, and we want to fit this to training data, and then make predictions on test data. There is a well-known danger that out-of-sample predictions will be poor, due to "...
0
votes
0answers
23 views

Convert prediction for differenced Time Series ARIMA(1,0,1) and ARIMA (1,1,1)

I am working on a Time Series model, and the series appeared to be non-stationary (presence of trend). I tried 2 ways: 1) put original data into ARIMA(1,1,1) 2) manually difference first order ...
1
vote
0answers
22 views

How to Choose Error Distribution For Time-Series Model

I am modelling a set of time-series, and understand various models (ARIMA, AR, GARCH) allow for the inclusion of non-Gaussian error distributions. I am aware that, after fitting a time-series model, ...
1
vote
2answers
77 views

Understanding MASE Value

I've looked through many of the other posts concerning the Mean Absolute Scaled Error (MASE) forecast metric and haven't been able to sort out my problem just yet. I'm working with some weather ...
3
votes
1answer
173 views

Predictor for averaged Brownian motion

The best forecast (predictor) for a Brownian motion at time $t+h$ is the present value at time $t$ since it's a martingale. The same holds for random walks with independent steps and without drift. ...
0
votes
1answer
59 views

Forecasting high-frequency electricity data with multiple seasonalities

for a school project I need to forecast high-frequency data using different methods of my choice. Data: I have hourly data on day-ahead electricity prices and a few other variables (hourly power ...
1
vote
2answers
57 views

Trend & Seasonality Determination in Time Series without looking at Graph [duplicate]

Most of the articles I have read describe determining Trends and Seasonal (TS) effects through rolling your eyes on Graphs. Graph is a nice visual representation, but I am looking a way in either ...
1
vote
1answer
252 views

Root causes of error in a forecast consisting of two multiplicative factors

I have a warehouse that packages units and ships them. Any number of units can go into the same package, including only 1 unit. I have a forecast for number of units and units per package (UPP). From ...
0
votes
1answer
233 views

Demand bottom-up forecasting and substitution effect

If retailer has many products the is likely to be a substitution effect within product groups (clusters). Hence, there is a notion of the "unit of demand" that is supposed to gather products based ...
0
votes
1answer
269 views

Which algorithm for forecasting a binary time series? [duplicate]

I would like to write the code to forecast the status. The status 0 means non-active, 1 means active. I would like to predict the future month (e.g 2016/6/1), the status should be "0" or "1". What ...
2
votes
1answer
58 views

What methods are available for forecasting with a sample of the data

In predictive analytics, specifically forecasting, what methods are available for getting the same predictive accuracy with $n$ (a sample of the data) which would be achievable with $N$ (all of the ...
0
votes
0answers
19 views

Why naive (prediction) forecasting is called random walk?

Why naive (prediction) forecasting is called a random walk? Naive prediction is to use the last value as a forecast. (It's clear that the best prediction for a random walk is a naive one.)
2
votes
0answers
57 views

Why is the propriety of a scoring rule irrelevant for deterministic forecasts?

By deterministic forecasts Jolliffe (2008) has in mind forecasts to which no representation of uncertainty is attached. Jolliffe (2008) p26 provides a standard explanation of proper scoring rules, ...
0
votes
1answer
238 views

Manually calculate SARIMAX forecast

I'm trying to manually replicate the forecast that I obtained using statsmodels.api sarimax (python). Its actually just an AR(1) model with one exogenous variable, in the form of SARIMAX(1,0,0)(0,0,0)...
0
votes
1answer
371 views

How to repoduce the fitted values from a forecast::Arima in R by hand?

We have fit an ARIMA (1,0,0) with exogenous reggressors using the forecast package in R and would like to write about this model. However when we write out the ...
2
votes
1answer
66 views

Why aren't my variables correlated?

I am currently doing a project on Load forecasting and it is known that in my country the temperature effects the load. I have hourly readings of Load and Temperature from the period between 01-...
0
votes
0answers
21 views

Why are my neural network predictions so wrong when I add another variable

I have created a neural network in order to predict the following hours electrical demand depending on the previous sixty observations. However I know that temperature affects the load at a given hour ...
3
votes
1answer
338 views

How to judge whether to model a time series additively or multiplicatively?

I don't know how to to identify whether my time series is additive or multiplicative using decompose() command in R. It is a monthly time series.
2
votes
2answers
33 views

Forecasting a Step-Like Time Series

I have an interesting time series dataset. I have monthly data and I would like to forecast the next 12 months of data points. I know the dates at which the dependent variable 'may' change up or ...
1
vote
1answer
24 views

Including regressors to improve forecasts on white noise

I am conducting some time series forecasts using quite limited data, 13 years annually. Basically, I am trying to forecast companies emission totals using historical values. The historical data ...
4
votes
3answers
9k views

Forecast daily data with weekly and monthly seasonality using exponential smoothing

I have to forecast data that exhibits dual seasonality. For example, the first day of the week can show seasonality and also the first week of the month can show seasonality. I am planning to use ...
0
votes
1answer
352 views

Regression with ARIMA Errors for non-stationary timeseries: Mixing of stationary/non-stationary covariates?

Given I want to forecast e.g. monthly sales (dependent variable, likely non-stationary) with regression and ARIMA errors (ARIMA in R with xreg) I have e.g. two independent variables/covariates: ...
0
votes
1answer
245 views

How Neural Networks' prediction in R works on periodic data?

I have a data set x x <- c(1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4,5,5,5,5,6,6,6,6,7,7,7,7) As my entries with a period of 4. And ...
0
votes
1answer
235 views

Combining Intermittent Demand and ARIMA

I have a time series dataset, where a customer may purchase fuel one week and not purchase again for 2-3 weeks. I need to forecast when a customer is likely to purchase and how much they will spend. ...
2
votes
1answer
38 views

ARIMA for daily data over 5 years - forecast package

I have a question to auto.arima and seasonality. I have to analyze 39 single datasets which are prices of futures or equities. There are missing data which I replace with na.approx. Then I calculate ...
0
votes
1answer
20 views

Some Questions Regarding Very-Short -Term Forecasting

I am dealing with solar power output forecasting, but I am still new to forecasting. I would like to ask a few questions here: It seems that most literature forecasts solar irradiance, while solar ...
-1
votes
2answers
487 views

Auto.arima is not fitting the data well

I have two variables speed and vibration and you can see that speed causes vibration. I am trying to fit this using auto.arima. But when i plot the fitted model, it gives bad result ...
1
vote
1answer
35 views

Predicting n'th percentile [closed]

When we use prediction, we can only say levels. For example: We have 500 sample data for our walking range. And let's say 90 percentile is 16.0 km and 10th percentile is 0.78 km. Well, can only say ...
3
votes
1answer
249 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 ...
36
votes
4answers
71k views

Difference between forecast and prediction?

I was wondering what difference and relation are between forecast and prediction? Especially in time series and regression? For example, am I correct that: In time series, forecasting seems to mean ...
0
votes
1answer
36 views

Combinef in the R HTS package- net aggregation

When using the combinef function in Rob Hyndman's very useful hts library (soon to be in incorporated in the new fable library/tidyverts framework) is it possible to have subtraction at any level? ...
0
votes
1answer
21 views

Are conditional mean in an AR(1)-GARCH(1,1) equal for different GARCH(1,1) processes of the same data?

I have created a Markov-Switching GARCH model, where the volatility is defined to be switching between two different GARCH(1,1) processes. The data is assumed to have zero mean, where the data is ...
3
votes
1answer
3k views

Time series regression with lagged dependent and independent variables

I have monthly data for air passengers, oil price and unemployment. I'm trying to create a model to forecast air travel demand using oil price and unemployment as explanatory variables but are facing ...