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
2
votes
1answer
70 views

Can I give continuous rank probability score (CRPS) to Diebold-Mariano (DM) test?

I would like to use DM test for probabilistic forecasting case. My initial thinking was to give CRPS of two forecasting methods instead of raw forecast errors, where CRPS is calculated using ...
0
votes
0answers
20 views

Why BSTS model gives very different forecasts depending on length of testing set?

When I run a BSTS model in R, I get a finished model from a data set, and when I use that model to predict, for example, like this: predict(model3,newdata=futuredata) -> pred_1 I get a set of ...
0
votes
1answer
49 views

Sarima : Fitting on train subset works but fitting on whole train does not [closed]

I use SARIMAX from statsmodels.tsa.statespace.sarimax in Python. I have a simple one column energy consumption dataset with 27679 rows. The frequency is Hour. I do hyperparameters optimization ...
5
votes
1answer
66 views

Is ARIMAx a transfer function model?

I would like to know if the ARIMAx model is considered a transfer function model. If the answer is no, further explanation on what are differences would be appreciated.
1
vote
0answers
17 views

Future event prediction methodology

I have a data set such that each data point is an "event" with features $x_1, x_2, \dots, x_n$ and the year of its occurence $y$. I want to train a forecasting model that predicts when an event of ...
0
votes
1answer
31 views

Manually adjusting forecasting model bias

I am trying to build an efficient forecasting model to predict sales in the future. I managed to obtain a first pretty solid model using a LSTM network. However, it wasn't sensible enough to large ...
1
vote
2answers
62 views

Forecasting daily data with zeros in Python

I'm currently testing some forecasts on daily sales quantities. However, out of ~2000 observations I have 16 zeros. How should I approach this? It's mainly Sundays and holidays that holds zero as ...
0
votes
1answer
24 views

Time series forecasting: How can I adjust my forecast to incorporate external predictions at a different time scale?

I have information about past and future values that I want to incorporate into my timeseries model (experimenting with ARIMA and other models) in order to predict the future at a more granular ...
4
votes
2answers
105 views

Incorporating Prior Information Into Time Series Prediction

Suppose I have data on my child C's height measured every week. Presumably there is a positive trend, due to growth, and some noise due to measurement errors, and maybe even seasonality (winter boots ...
0
votes
0answers
34 views

Forecasting time series for categorical variables

I have time-series data with daily sales for shops and sold items. I would like to predict the number of each product sold in each store. What is the best way to solve this problem? It is necessary to ...
0
votes
0answers
12 views

Treating seasonality in Partial Least Squares forecast

I've been looking for answers on this question but couldn't find concrete solutions so wanted to ask y'all. I have been playing around trying to forecast an economic/financial-related indicator with ...
0
votes
1answer
40 views

Time series Data : Regress absolute values or regress the %growth of the values?

I am doing a time-series data analysis. The idea is to produce a forecast from the regression output. I am regressing Air traffic passengers of country A with GDP/capita of country A. I am getting ...
0
votes
0answers
15 views

time series forecasting - ljung-box test - degrees of freedom to subtract when working with breaks

I'm working on a differentiated seasonal time series with 2 breaks and non-zero mean. So, besides the constant i've got 2 dummies for breaks correction. Question: when performing LB test, if my model ...
0
votes
0answers
13 views

Vector Time Series: Capturing Systematic and Nonsystematic Patterns in Multiple Datasets | Financial Option Data

How does time series work with multiple time series data sets on the same index? For example, suppose I were a utilities company. Suppose I have the electricity usage of two homes, each indexed for ...
2
votes
0answers
129 views

Estimating prediction interval of ARMA process using R forecast function

the theme is forecasting with ARMA models. I'm trying to understand how the R forecast function works if applied to an ...
3
votes
2answers
92 views

Forecasting in a state-space model from a Bayesian perspective

We have the following state-space model(or linear dynamical model): \begin{align} x_t&\sim N(Ax_{t-1},Q)\\ y_t&\sim N(Bx_{t},\Sigma) \end{align} I want to obtain a sample from $p(y_{T+1}\mid ...
0
votes
0answers
17 views

How to use forecast data in neural net when forecast produced periodically?

I am considering how to structure a neural net problem where an input forecast (say, for chocolate production) is produced for 'k' time periods. This forecast is produced every time period. and I ...
1
vote
2answers
102 views

Is my Data stationary? KPSS, ADF Tests and ACF

I already differenced my Data by 1 and i am not sure whether my Data is now stationary or not. I perfomed an KPSS and ADF test in order to help me decide if it is. I think it is stationary but im not ...
0
votes
0answers
17 views

ARMA Forecasting - Professional Work

I was curious how long does it take you to do ARMA forecasts in your professional environments? I'm getting started using the "Real Statistics" Add-On in Excel & I have only been familiar with ...
0
votes
2answers
47 views

How are missing data handled in Time series estimation?

I am looking for most popular/theoretically sound methods for handling missing data in time series model (particularly ARMA class) estimation. Also what method is used in R (in arima and in forecast ...
0
votes
2answers
254 views

Multi-step ahead forecasting with LSTM neural network

I would like to forecast the heat load of a district heating network given its past values, the temperature and the 3-day ahead forecast of the temperature with an LSTM RNN. The data is hourly and I ...
1
vote
1answer
103 views

Forecasting recurring orders for an online subscription business using Facebook Prophet and R

I am analyzing data from a subscription model, in which a customer must pay a recurring price at a regular interval (30 days) for access to the product. EDIT -> Direct link to daily data: https://...
1
vote
0answers
46 views

How do you evaluate bias and/or quality of time-series forecasts

I am working on a financial model that will forecast the revenue a company generates over a fiscal quarter, and I am not sure of the best way to rigorously evaluate the bias in the model. Every day ...
0
votes
1answer
41 views

Modeling non-linear (short) time series and cross-validate them

beginner data scientist here. Time series analysis is a completly new area for me, so please correct me if i write something that makes no sense. I have many multivariante short time series, between ...
1
vote
2answers
128 views

Predicting walking routes using PyTorch

I'm working on a project that uses sensors to monitor a persons location. These devices simply record the current GPS coordinates and ping them back to a server (the coordinates will then be converted ...
0
votes
0answers
38 views

how to calculate safety stock from output of ARIMA model?

I have built an arima model using monthly sales as input suppose the output from ARIMA model is : How do we calculate safety stock for different lead times lead times (in days)?? ...
1
vote
1answer
37 views

multivariate time series: selecting a predictive model

I have a time series dataset that looks like this ...
1
vote
0answers
29 views

How to get quantiles/probabilities of time series forecasts?

my problem is as follows : I am creating demand forecasts for some goods with different methods (ARIMA, ETS,..) The issue is that I would like to forecast the probabilities of those forecasts since ...
3
votes
3answers
330 views

Deep Learning based time series forecasting

According to the paper "Statistical and Machine Learning forecasting methods: Concerns and ways forward", it looks like the recent DNN-based approach has weaker predictive power in extrapolation, i.e. ...
3
votes
0answers
19 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 ...
0
votes
0answers
16 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
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
32 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
24 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 ...
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) ...
0
votes
1answer
46 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 ...
2
votes
1answer
193 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, ...
2
votes
1answer
46 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. ...
2
votes
2answers
38 views

Median-based Versus Average-based forecast? Which is better?

When generating forecasts (e.g., product-customer time series data), should we choose an average-based forecast or median-based forecast? I recently read a very nice article by Nicholas Vandeput on ...
2
votes
1answer
52 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
0answers
26 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
23 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, ...
0
votes
2answers
31 views

How to Recursively Predict a Time Series Using Neural Networks

I am currently using neural networks to forecast an electrical demand time series. I am trying to create a forecast for the following day given previous observations at half hourly intervals. My ...
4
votes
3answers
105 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 ...
4
votes
1answer
55 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 "...
1
vote
2answers
61 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
2answers
92 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 ...
0
votes
0answers
123 views

Big Mart Sales Prediction Problem

I hope that some of you are familiar with Big Mart sales prediction data that was provided by Analytics Vidhya as a contest. The problem statement of on the website is as follows: The data ...
0
votes
0answers
27 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.)
0
votes
0answers
24 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 ...