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
0
votes
0answers
22 views

What is the best model to forecast ACT scores using practice test scores and past student data?

I understand that I may not be asking this question correctly, and would appreciate any feedback possible in order to help set me on the path to figuring this out... I work at a high school where ...
6
votes
1answer
253 views

Model for spatiotemporal and discrete variables

I have a situation where I am monitoring events at 50 or so geographical sites in a town and at each of these sites, I am making measurements regarding the count of certain particles (so the ...
2
votes
0answers
26 views

ARIMA predictors - clarification

I'm working on multivariate time series (still), and would like some clarification. I was reading this site: Duke Forecasting and I came across this statement: "We see that the most significant ...
1
vote
1answer
43 views

acf and pacf suggests MA but auto.arima gave AR

I have this data which is residual series obtained from predicted values and observations. original series was a random walk with a very small drift(mean=0.0025). ...
1
vote
1answer
19 views

Forecast existed ARIMA model using primer time-series

I have some fitted ARIMA model: ...
0
votes
0answers
21 views

How to properly add spatial features for a precipitation time series forecasting?

I am reading this paper. The center of the circle is the site where the model should forecast precipitation. Red stars in the picture are nearby sites and each site has these features: I want to ...
1
vote
1answer
164 views

Getting best fitted model using Auto ARIMA but prediction result is very bad

I saw this: time series - Poor prediction using ARIMA model But the answers aren't clear and isn't directing to me for solving the problem I have. Using only AR is giving me better prediction whereas ...
1
vote
0answers
24 views

Forecast efficiency: why no correlation between errors and available information?

(Applied Economic Forecasting using Time Series methods; Ghysels, Marcellino, 2018), in the chapter about forecast evaluation, relates efficiency as "the efficient use of the available information". ...
0
votes
0answers
29 views

Kalman filter on stock sentiment time series

I was wondering if & how I can use a Kalman filter on my dataset which contains closing prices of stocks + sentiment scores of tweets about that stock for each day in a timeframe of 1 month. e.g....
0
votes
0answers
37 views

How to choose the right forecast method for variable 'X' when I have some available forecast for variable 'Y' with historical data of X and Y?

I have yearly historical data for variables 'X' and 'Y'. Say the time frame is 't'. In addition to available historical data, I also have the forecast data of variable 'Y' for t+1. My aim is to ...
2
votes
3answers
257 views

Can my data be white noise if the mean >0?

According to the auto-correlation method, my time-series is white noise (i.e. 95% of ACF within ±2/√T), yet the data are counts and thus the mean >0. Are these two facts incompatible? I'm using the ...
0
votes
0answers
10 views

How to calculate forecast given variation by day

I'm trying to work through a problem and I'm wondering if I'm interpreting it correctly. Let's say we predict the price of stock (today worth $50) to vary by N~(0,1) every day, and you are looking to ...
0
votes
0answers
16 views

How does forecast skill score change when seasonality in the forecast quantity is removed?

Given RMSE skill score $s$: \begin{equation}\label{eq:msess} s = 1-\frac{\text{RMSE}(f,x)}{\text{RMSE}(r,x)}, \end{equation} where $f$, $r$, and $x$ are forecasts of interest, reference forecasts, ...
2
votes
2answers
124 views

Fitting a GARCH model and forecast using validation set approach In R

I have seperated the data into training and testing data. Then I fitted this simple garch model for training data as follows,(using rugarch package) ...
4
votes
2answers
106 views

Time Series: Confused about identification of (possibly?) an ARMA(p,q) model

this is my first ever question on a website i use frequently! This time series has given me much trouble over the last couple of days even after extensive googling, I suppose with TS theres no two ...
2
votes
1answer
45 views

What is the difference between probabilistic forecasting and quantile forecasting?

A probabilistic time series forecast outputs the entire distribution of the forecasted values for a given time point, instead of just a mean or a point forecast. A quantile forecast is a forecast ...
0
votes
0answers
32 views

Feature engineering suggestion

I've to forecast the revenue generated by a company on a monthly basis. The dataset looks like this: ...
2
votes
1answer
150 views

Dummy/baseline models for time series forecasting

I am working on an evaluation of time series forecasting models in Python, more specifically with statsmodels, scikit-learn and tensorflow. I think it makes sense to first compare the model ...
1
vote
1answer
119 views

Need help with lag features in regression forecasting

I am trying to build a timeseries prediction model. The problem is that I'm still hesitant whether I should use lag features or not. What makes me wonder is the fact that the training data has these '...
0
votes
0answers
28 views

Forecasting a year ahead using annual vs daily data

Suppose, as an example, that you would like to forecast a share price in a year's time based on the past 20 years of data. You can either use annual data and forecast 1 period ahead, or use daily data ...
2
votes
0answers
28 views

Forecasting using MA(2) model when past 5 observations are known

So given an MA(2) model : Xt = Wt + Theta1 * Wt-1 + Theta2 * Wt-2 Where Wt is white noise. (Normally distributed) and Theta1 and theta2 were available. Say if X96,X97,...X100 of the series were given ...
3
votes
2answers
452 views

Why is arima in R one time step off?

I've recently noticed an odd behavior in a few timeseries methods. Let's fit an arima model (ar1) to the annual subspots data ...
0
votes
0answers
32 views

Elastic net chooses lags beyond ACF cutoff

I've been using Elastic net for time series forecasting. I’m using first difference of the series. Normally I use the ACF to determine the number of lags to use. I was curious, if I would produce more ...
0
votes
0answers
7 views

How to handle partial observations of the variable of interest when training a time series model?

I have the following time series data: $\{ t_i, X_i, Y_i \}$ where $i$ is the index, $t_i$ is the timestamp, $X_i$ the measured value of the external variable and $Y_{i}$ the value of the variable ...
0
votes
1answer
38 views

Linear Forecasting with a small dataset

I am trying to get some forecast (5 years more) from a small dataset that is as follows: ...
0
votes
0answers
12 views

Algorithm for producing a Moving Average (as in ARIMA) model

I have a time series $X_t$ and I want to produce an ARMA forecast (without using any automated packages - the purpose of my project is to understand how those work). So far, I have the AR(p) part ...
0
votes
0answers
23 views

Forecasting with time series with different interval

I am trying to forecast monthly inflation rates using weekly percentage change of commodities prices. Is there a way to do this without losing info? or can I like get the predictor's moving average ...
1
vote
1answer
109 views

auto.arima throws error wrong length for 'fixed' [closed]

I am using auto.arima from the R forecast package. When using this function with lambda parameter, it is throwing error wrong length for 'fixed' Here is the code ...
3
votes
1answer
247 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 ...
1
vote
1answer
73 views

Forecasting with AR(1) and pseudo out-of-sample using R

I'm trying to do Pseudo out-of-sample forecasting using R. And, I also have the following initial data (gdp) ...
3
votes
3answers
352 views

Does lack of seasonality imply random time series?

Some techniques for time series analysis (prediction) require that the time series not have seasonality. It seems that without seasonality, a time series is essentially random, in which case ...
0
votes
1answer
95 views

K in Fourier series - How to find value of K to use it in ARIMA?

I am using the forecast library for doing some time series forecasting. I need to forecast number of sold items. I am planning to add holidays as xreg in auto.arima. The holiday will be a 0/1 list, ...
0
votes
0answers
23 views

How to get a forecast equation for $\hat{y}$ using ETS state space model

The ets(AAA) state space model (Rob Hyndman's handbook) is as below State equation is \begin{equation} Y_t = L_{t-1} + b_{t-1} + S_{t - m} + \varepsilon_t \end{equation} The measurement equations ...
0
votes
0answers
51 views

Dynamic regression with lagged explanatory variables

I have data on unemployment from 2006 to 2018(monthly) and have fitted a $sARIMA(3,1,1)(0,1,1)_{12}$ that has decent forecasting abilities, however I want to try to improve the forecasting abilities. ...
0
votes
0answers
15 views

Timeseries forecasting repeadted results

I'm trying to fit GBR on a timeseries of profit of a company. The code looks like this: ...
0
votes
2answers
42 views

Which tools should I learn to use in order to forecast sales for each day?

I am trying to forecast sales for a company that runs a few stores. In many cases, I am pretty successful using some basic methods in Excel to forecast sales for every month, but I'd like to be more ...
2
votes
1answer
43 views

Forecasting sales (in units) for thousand of products

I've got into this internship in a retail company and they asked me to think a way to forecast their daily sales (in units) in all their stores (with thousands of skus each one). At first I thought ...
2
votes
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 ...
1
vote
1answer
59 views

Monthly Times Series Modeling Approach

I have a machine learning problem and have been working in Sklearn/Pandas with Python to come up with an accurate model. I find myself deep in a rabbit hole trying to learn the best approach and how ...
2
votes
2answers
44 views

Time series forecasting with hour data, prediction for next 24 hours

i'm a newbie in Time Series Analysis. I have a 2 year pandas dataframe about water consumptions in hour granularity (24 records for day, 365 days). Water_consumptions Data ...
0
votes
0answers
11 views

Analysis for multiple products with some having 0 values for the entire dataset

My question is pertaining to automatic forecast of multiple products. I am using a combination of 2 models to forecast my timeseries data for 190 products. The values are arranged in column format. ...
1
vote
1answer
31 views

Using lagged explanatory variables to forecast future value of depended

Is there a way or method to use older values (lagged) of independent variables with alternative lags to explain current value of dependent variable? For time series specific
0
votes
1answer
18 views

Can we do day level forecast clubbing all the data of single day?

I've day level sales data. If I select let say all monday sales from this data. Can I make a time series of all Monday sales which can accurately predict the future. If I can, how reliable is that ...
0
votes
1answer
37 views

ARIMA model on daily page views data

We need to forecast the daily web page views data. Data consists of a csv file starting with 1st January 2016 and ends with 7th February 2019. Total no. of daily records is 1090. Following step by ...
0
votes
0answers
58 views

Why is the linear model obtained using linear regression with ARIMA errors different from the simple linear model

This question is basically to get a better understanding on how linear regression with ARIMA errors models are fitted. From reading online resources on this method, e.g., Prof. Rob Hyndman's online ...
1
vote
3answers
355 views

XGboost for Time series - using lag of target variables

I'm trying to make a time series forecast using XGBoost. I have already added many time related variables - day_of_week, month, week_of_month, holiday. I want to add lagged values of target variable ...
1
vote
1answer
95 views

Residual diagnostics for seasonal ARIMA model, time series analysis

Im a novice in time series and currently experimenting abit with time series forecasting. I have gathered monthly unemployment data for 23 years for a country, and want to do some forecasting. From ...
0
votes
0answers
49 views

Predicting future price in high inflation economies

I am trying to create a machine learning model in a country which has high inflation. With this model, I am trying to predict the price of a second hand car. As my train data, I have second hand car ...
0
votes
0answers
14 views

How do I analyze time series with less variation in values?

I know i am asking a very generic question but this is something that i encountered in one of my projects. I am working on churn prediction for a bank and one of the features that i was using average ...