Given real$-$world data for weekly Texas regular conventional retail gasoline prices $($in Dollars

per Gallon$),$ sourced from the U$.$S$.$ Energy Information Administration, please make

predictions for the retail price of regular gasoline disclosed in the week following next week

$(03/18/2024)$ Sourcekey EMM$\_$EPMRU$\_$PTE$\_$STX$\_$DPG

Date Weekly Texas Regular Conventional Retail Gasoline Prices $($Dollars per Gallon$)$

Jan $02,20232\mathrm{.}841$

Jan $09,20232\mathrm{.}845$

Jan $16,20232\mathrm{.}946$

Jan $23,20233\mathrm{.}048$

Jan $30,20233\mathrm{.}119$

Feb $06,20233\mathrm{.}045$

Feb $13,20233\mathrm{.}004$

Feb $20,20232\mathrm{.}987$

Feb $27,20232\mathrm{.}889$

Mar $06,20232\mathrm{.}955$

Mar $13,20233\mathrm{.}083$

Mar $20,20233\mathrm{.}019$

Mar $27,20233\mathrm{.}069$

Apr $03,20233\mathrm{.}113$

Apr $10,20233\mathrm{.}222$

Apr $17,20233\mathrm{.}351$

Apr $24,20233\mathrm{.}238$

May $01,20233\mathrm{.}107$

May $08,20233\mathrm{.}016$

May $15,20233\mathrm{.}075$

May $22,20233\mathrm{.}024$

May $29,20233\mathrm{.}115$

Jun $05,20233\mathrm{.}055$

Jun $12,20233\mathrm{.}155$

Jun $19,20233\mathrm{.}089$

Jun $26,20233\mathrm{.}21$

Jul $03,20233\mathrm{.}11$

Jul $10,20233\mathrm{.}176$

Jul $17,20233\mathrm{.}154$

Jul $24,20233\mathrm{.}264$

Jul $31,20233\mathrm{.}449$

Aug $07,20233\mathrm{.}463$

Aug $14,20233\mathrm{.}407$

Aug $21,20233\mathrm{.}466$

Aug $28,20233\mathrm{.}399$

Sep $04,20233\mathrm{.}378$

Sep $11,20233\mathrm{.}347$

Sep $18,20233\mathrm{.}458$

Sep $25,20233\mathrm{.}367$

Oct $02,20233\mathrm{.}319$

Oct $09,20233\mathrm{.}181$

Oct $16,20233\mathrm{.}021$

Oct $23,20233\mathrm{.}001$

Oct $30,20232\mathrm{.}925$

Nov $06,20232\mathrm{.}835$

Nov $13,20232\mathrm{.}739$

Nov $20,20232\mathrm{.}726$

Nov $27,20232\mathrm{.}636$

Dec $04,20232\mathrm{.}7$

Dec $11,20232\mathrm{.}598$

Dec $18,20232\mathrm{.}504$

Dec $25,20232\mathrm{.}614$

Jan $01,20242\mathrm{.}631$

Jan $08,20242\mathrm{.}61$

Jan $15,20242\mathrm{.}645$

Jan $22,20242\mathrm{.}657$

Jan $29,20242\mathrm{.}735$

Feb $05,20242\mathrm{.}805$

Feb $12,20242\mathrm{.}791$

Feb $19,20242\mathrm{.}891$

Feb $26,20242\mathrm{.}813$

Mar $04,20242\mathrm{.}902$

Mar $11,20242\mathrm{.}882$

Mar $18,2024$ The price you are required to predict. $1.$ Weighted Moving Average Method: The length of the time window and the weights

assigned to each data point should be thoughtfully selected through your analysis, for

example, using trial and error. Please provide a brief explanation of your selections, along

with a detailed analysis and calculation process. $(10$ points$)$

$2.$ Exponential Smoothing Method: Choose an appropriate $$ through your understanding

or analysis, like trial and error. Explain your selection and provide a detailed analysis and

calculation process. $(10$ points$)$

$3.$ Linear Regression Method: Provide a detailed analysis and calculation process. Note that

the entire time$-$series data is NOT LINEAR, so it$$s highly recommended to use a portion

of the data for forecasting. $(10$ points$)$

$4.$ Forecasting Accuracy: No action is required for this question. Your score will be based

on the accuracy of your best forecast $($from one of the three methods above$)$ compared to

the actual price of regular gasoline in Texas for the week after next week. The actual

price for your predictions will be disclosed on $03/18/2024.(10$ points$)$

Tips:

a$.$ You can choose to use part $($e$.$g$.,$ the most recent data$)$ of the given data in your

predictions. If you decide to do this, please give a brief explanation of it$.$

b$.$ You can use mean absolute percent error $($MAPE$)$ or mean absolute deviation $($MAD$)$ to

measure your forecast errors, then decide the required parameters in each forecasting

model.