Questions tagged [forecasting]
Prediction of the future events. It is a special case of [prediction], in the context of [time-series].
936
questions with no upvoted or accepted answers
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1answer
52 views
Data leakage when using walk forward optimization
I am setting up a neural network that will predict the incoming customers at a store for the next seven days (the output is a list with seven numbers, one for each day). As input, I will give the ...
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143 views
The Efficient Market Hypothesis and forecastability?
According to Wikipedia:
The efficient-market hypothesis (EMH) is a theory in financial economics that states that asset prices fully reflect all available information. A direct implication is that ...
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65 views
auto.arima picks seasonal model for non seasonal series
I have a monthly time series data of 3 years whose acf and pacf plots confirms absence of seasonality. But auto.arima picks a seasonal model by seasonal difference first and then seasonal AR component ...
2
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0answers
103 views
Obtaining from scratch the volatility in GARCH model using R?
I'm trying to obtain the same vector of volatility by myself $\sqrt{h_{t|t-1}}$ of a Garch Model, that I obtained "automatically" using the function "ugarchfit" from the package "rugarch".
So after ...
2
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1answer
108 views
Flat forecast of trended time series data in r
I have a monthly time series of online visits for last 3 years starting from Jan 2016 to Dec 2018 and need to forecast for 2019. The data clearly has an upward trend although no seasonal lags ...
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66 views
Can the sum of several time-series be a white noise process, when the individual time series are not?
Intuitively, I think that it is possible for a sum of time series to be white noise, when the individual time series are not. Reason I am asking, is because I want to know if it's useful to ...
2
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1answer
35 views
How can a person predicted best playing 11 in a match between two teams?
This website allows people to bet on cricket and football matches. They ask people to select 11 players and there are point system, so at the end whoever ends with more points gets lots of money.
...
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39 views
Time Series Prediction Model for Home Prices
I am building a time series model to predict the zillow home prices for march 2019.I have data for each zip code from the year 1993 - 2018 and i have prices for every month.I was trying to use ARIIMA ...
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37 views
Using maximum of forecasted values to forecast maximum
I am using an algortihm to generate a daily sales Forecast and have concluded that the Forecast is, for pratical purposes, of good enough quality ("low" wMAPE).
In general, and without further ...
2
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0answers
54 views
How can I normalize truncated variables for a neural network?
Generally, I normalize variables using standard normal variates or (x-xmin)/(xmax-xmin)
But this only works well for variables that are not truncated, for example ...
2
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2answers
106 views
Rolling Window Forecasting with ARIMAX while supplying actual values
I am comparing different exogenous variables in how good they support the forecast of the monthly seasonal adjusted unemployment rate. All my data is monthly (2006-01-01 until 2018-09-01) and ...
2
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104 views
Is the forecasting model I am using overfitting and what is the best place to end training?
I am working on a forecasting model for natural gas consumption. I have many exogenous variables and when I train the data with the nnetar model(using R and the forecast packagae), one can specify the ...
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106 views
Convergence of predictions of an autoregressive model
I have performed a simple autogregressive model with lag 2 on a time series data. After obtaining the coefficients, I have computed the predictions. Since the lag is 2 in model, the first prediction $\...
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42 views
Forecasting autoregressive model. What's the best linear predictor?
Obviously if $X_t = \phi X_{t-1} + Z_t$, then the best linear predictor of $X_t$ given $X_{t-1}$ is $X_t = \phi X_{t-1}$.
But if $\phi$ is unknown, one may attempt to substitute $\phi$ by a Yule-...
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0answers
89 views
Interpreting forecast predictions of log transformed data
Using the forecast function in R, I make a 1-step prediction for a log-transformed data set Y, ( Y = log(X) ). This prediction gives me a mean and a 95% prediction interval.
How valid is this ...
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42 views
Are there any other models besides ARMA models that require stationarity?
Every now and then I come across a discussion of forecasting methods that mentions the topic of stationary time series vaguely without specifying that it is a question mainly in the context of ARMA ...
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0answers
82 views
How to interpret model confidence set in R
I want to compare 8 different forecast models to each other. Since I dont want to run into the $\alpha$-Inflation of multiple testing I heard about the model confidence set form Hansen.
I did this ...
2
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1answer
83 views
How can I split my time-series data into Train/Validation and Test set to apply Rolling Window
I am dealing with the LASSO regression in (pure AR-regressions) context. I have a lot of observations (around 4000). Therefore I would try to use the train/validation/test method. The idea was to ...
2
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0answers
47 views
Computing a corrective regression forecasting factor
I am working on forecasting problem using a regression model like gradient boosting to predict the number of weekly sold shoes.
I am using the historical data only from last year to predict the sales ...
2
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0answers
42 views
Evaluating which forecasting method works better? Statistical or Business Forecast
Somewhat new to the forecasting area. I am trying to evaluate whether the statistical forecasts are better than manually generated forecasts in one of our used cases. I have 1000s of customers who ...
2
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0answers
35 views
Testing time-series forecasts against actual observations
I'm conducting an event study on annual executive salaries.
I have a sample of 52 companies which have been given a cartel fine during year 6 (Event year).
For each company, I have a time series of ...
2
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1answer
216 views
ARIMA(1,1,1) Model - Forecast
How does one
write the mathematical equation for the ARIMA(1,1,1) model with the estimated coefficients below and
use the ARIMA(1,1,1) model and time series points below to produce a forecast value ...
2
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0answers
163 views
How can a combination of Random Forest and Linear Regression improve a time series forecast?
I attended a presentation by some consultants for retail demand forecasting who showed that for one of their clients, they were able to improve their demand forecasting by replacing a traditional time ...
2
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1answer
31 views
Establishing the minimum required training set size, when cross validating time series data
I want to evaluate and compare how well various models perform with regards to modelling time series data (the data in question is daily revenue). It seems that cross validation error might be a ...
2
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0answers
937 views
How does neural network auto-regression produce multistep forecasts?
I am looking at time series forecasting using neural networks as described in Hyndman and Athanasopoulos.
They describe Neural Network Auto-Regression models as non-linear generalizations of AR ...
2
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0answers
182 views
How to forecast hierarchical time series with external unique external regressors for each base time series?
I have hierarchical time series with 70 base time series, forming 4 level of hierarchies. I am using forecast() function in R from the package forecast.
The ...
2
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0answers
36 views
Out-of-sample forecasts: Why does model with log-transformed variables perform so much better?
I am developing a model to forecast the number of students enrolled in roughly 65 primary schools in a large city. Relevant predictors include the number of appropriately aged children living in the ...
2
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0answers
127 views
Implementaiton of Continuous Ranked Probability Score (CRPS) when Observation is a Distribution
The most general form of the Continuous Ranked Probability Score (CRPS) is defined as,
$\int_{\mathbb{R}} \big( \hat{F}^e(x) - F^0(x)\big)^2dx,$
for some true distribution, $F^0$, and empirical ...
2
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0answers
88 views
Explaining stationarity in a visual way
I am new to forecasting and want to try and explain to my peers in a visual and simple way how you know if a time series is stationary or not.
In the forecasting books I have read, the advice is ...
2
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1answer
46 views
Effect of strong auto-correlation on forecasting?
Suppose a wise-sense stationary univariate time series has relatively strong auto-correlation of lag-length of 1, say, around -0.7
Then how would it affect the forecast?
Conversely, if a ...
2
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0answers
74 views
Assign less weight to most current observations in forecasting
in forecasting, typically, we assign a heavier weight to the most current observations. However, I am finding many cases where a "blip" in last month's sales leads to a very pessimistic view about the ...
2
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0answers
130 views
Why does stl() decomposition require integer frequency?
I need to decompose and forecast weekly series with around 10 years of data. In this data leap years play an important role so I need the have non-integer frequency, frequency = (365.25/7)
By ...
2
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0answers
140 views
Are there any rules of thumb for the number of hidden layer neurons in a RNN or LSTM for time series prediction?
Say that I have a univariate time series X(t) that I want to forecast using RNN/LSTM.
I have 2 years of weekly sales data that is seasonal. How many hidden layers and neurons in each layer do I need ...
2
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0answers
321 views
Accuracy measures in training/test split of time series
I'm using Forecast Principles and Practice 2 to study time series and a doubt came in mind while I was trying to do exercise 7 of chapter 3.
How sensitive are the accuracy measures to the training/...
2
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0answers
62 views
Forecasting costs with forecast interval using past performance
I'm trying to adopt a model for project cost forecasting in agile.
Consider the following table of previous costs per sprint, along with story points completed:
...
2
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0answers
143 views
Tree model does't go well on trend
I am using sales time series data 2011 onwards, to make predictions for upto 2 years. Other than date and holiday related features, i created moving averages, y/y ratios and lags. I also extracted ...
2
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1answer
89 views
ARIMA forecasting using exogenous variables with their own forecast intervals
Suppose
model <- Arima(y , xreg=cbind(x1, x2), order=(p,d,q))
If I am forecasting $x_1$ and $x_2$, then for forecasting $y$:
1) If I use expected forecasts ...
2
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0answers
257 views
Is there any interpretation of parameters in Holt Winters method?
I am doing forecast on time series on R and I use exponential smoothing method Holt Winters.
Does a value of $\alpha$ close to $0$ or $1$ "mean" something particular about the series?
Same question ...
2
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0answers
81 views
What machine learning techniques to use to predict for multiple seperate sequences of time-series data?
I am having difficulty structuring my data and finding a machine learning technique to predict my outcome.
My data: I have a number of users with observations of a number of factors each year, each ...
2
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0answers
237 views
How much do the parameters in the Holt-Winters model matter?
When fitting a Holt-Winters model, I usually take the approach of retrospectively "predicting" some known historical values for the series, and optimising the coefficients for the parameters by ...
2
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1answer
290 views
A time series logit model with lagged dependent variable
I have a panel dataset for stocks. My goal is to model and predict if the stock will close positive (1) tomorrow based on today's close (1/0) and other macroeconomic and firm-specific variables.So I ...
2
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0answers
422 views
Alternatives to Holt-Winters models when the seasonality pattern has changed
I am forecasting a series of daily volumes in terms of units processed for a particular time period (the period around Christmas).
Historically, I have used a Holt-Winters model, with the minor ...
2
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0answers
227 views
Building the covariance matrix for hts prediction intervals
In my previous question: Using information about covariance between ARIMA models in forecasting I was interested in the more general case of how to use the covariance matrix in prediction intervals ...
2
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0answers
188 views
How Negative Binomial Distribution and negative bionomial regression can be used to sales forecast?
My first question here. Due to the improper inventory management we seem to have dispersed sales, and the stores are unable to meet the demand because items are being out of stock. There are so much ...
2
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0answers
42 views
Neural Networks for predicting Energy at particular date
I am trying to predict Solar Energy value at particular date.So,for this I am applying Artificial Neural Networks model.I am having problem in deciding activation function. Since sigmoid function ...
2
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0answers
195 views
Best measure for multiple time series modelling prediction methods?
Newbie question, sorry. I have a highly seasonal monthly time series, predictable with no exogenous/independent variables and no obvious trend. I want to show that a suitable state space model (using <...
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0answers
19 views
Scaling prediction from VAR model subject to a equality constraint
I have a forecasting problem and already built a decently working VAR model which provides forecasts as $\hat{Y}_{iT}$, for $i = 1,..n$ and $T$ is forecast time period.
But now I have an additional ...
2
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0answers
33 views
How to know what is the frequency of our data and how to make choice of which model is best for our data to forecast
I have a data for each minute as shown below
...
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0answers
52 views
Forecasting method used for predicting the date of some events
If i'm working in a car company and I have some data for every customer i.e.
Their license plate
Date of their car's service in the dealer
Number of km in their car when they service it
Their ...
2
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0answers
755 views
Repeated arima forecast returning warning and NA value
I have the code below which trains a model with some predictors, forecasts it one step, appends the forecasted value on the original training data and then tries to feed that back in and train and ...
